require(knitr)
    require(png)
    require(dplyr)
    require(stringr)
    require(metafor)
    require(compute.es)
    require(MuMIn); eval(metafor:::.MuMIn)
    require(kableExtra)
    require(pander)
    require(ggplot2)
    require(RColorBrewer)
  
    source("src/acoustic_indices_functions.R")

This page describes the systematic review procedure and meta-analytic steps we took while assessing if acoustic indices are reliable indicators of biodiversity.

Systematic review

We extensively searched existing literature for studies assessing the use of acoustic indices as proxies of biodiversity. The systematic search proceeded as follows:

  1. We compiled studies that used acoustic diversity indices from four recent literature reviews on acoustic indices, biodiversity assessment and passive acoustic monitoring (i.e. (Sueur et al., 2014; Buxton et al., 2018a, 2018b; Sugai et al., 2019)).
  2. We updated the literature database from 2017 up to July 2019 with Thompson’s ISI Web of Science (WoS), querying the database for the keywords (i.e. bioacoustic∗ AND ind∗, ecoacoustic∗, acoustic∗ AND biodiversity, soundscape AND ecology). This search was restricted to 9 WoS subject areas (i.e., zoology, environmental sciences ecology, behavioural sciences, biodiversity conservation, marine freshwater biology, acoustics, evolutionary biology, entomology, and remote sensing).
  3. Additionally, we used Google Scholar, to compile all literature from 2017 to July 2019 that cited the papers originally describing the eleven selected indices as well as literature that cited the recent papers published within this period.

Both peer-reviewed and no peer-reviewed studies were included to avoid publication bias.

After removing all duplicates, we gathered a total of 1,038 studies.

Inclusion criteria

We considered studies eligible for meta-analysis if they met the following inclusion criteria:

  • The study reported data to test the efficiency of acoustic diversity indices as indicators of biodiversity.
  • The study provided statistical or graphical information of the direct relationship between biological diversity and acoustic diversity.
  • We only considered studies using the 11 most common alpha acoustic indices (ACI, ADI, AEI, AR, BIO, ,H, Hf, Ht, M, NDSI, NP).

Following such inclusion criteria, we screened all selected studies (n = 1,038) based on their abstract, titles and keywords and thereby retaining 142 studies that were identified as potentially eligible. To ascertain their relevance, we conducted a full-text assessment on all these studies, finally retaining 34 studies that passed through all the criteria.

Data extraction

Main extracted data

For each study:

  • We retrieved the acoustic indices calculated and related to biological diversity. The selected indices were: ACI, ADI, AEI, AR, BIO, H, Hf, Ht, M, NDSI, NP.
  • We classified the diversity estimates that were measured and related to acoustic indices into five types: Abundance - number of individuals of a single or several species; Species richness - number of vocal and/or non-vocal species; Species diversity - Shannon index, Simpson index or species evenness; Sound abundance - number of sounds by known or unknown species.; Sound richness - number of sounds types by known or unknown species.
  • We described the method applied to obtain such biological information based on two variables: Acoustic - data extracted from audio recordings; Non-acoustic - from sources other than recordings, i.e. literature, field surveys, etc.
  • We included the environment type: Terrestrial; Aquatic.
  • And studied taxonomic groups: Invertebrates; Anurans; Fish; Birds; Mammals; Several.

Handling of pseudoreplication

To account for differences in sampling effort among studies and to support the detection of cases of pseudoreplication that might potentially lead to biased statistical tests, we extensively assessed the study design by identifying a total of ten features that summarized both spatial and temporal sampling of each eligible study. When a mismatch between sample size and the number of true replicates was identified, we classified the analysis as using inflated replication and used true replicates as our sample size for meta-analysis.

Effect size calculation

We used Pearson’s correlation coefficient r, as our measure of effect size. The effect size describes the magnitude of the relationship between acoustic indices and biodiversity. A positive correlation indicates a positive relationship between acoustic indices and biodiversity (i.e. a higher value for the acoustic index corresponds to higher biodiversity) whereas a negative correlation indicates a negative relationship between acoustic indices and biodiversity (i.e. a higher value for the acoustic index corresponds to lower biodiversity).

When Pearson’s correlation, r, could not be directly collected from the studies, we extracted other statistics, such as Spearman’s correlation, t-values, F-values, linear regression slope coefficients and R². If only graphical information was available we extracted the statistics with Web Plot Digitizer v4.2. (Rohatgi, 2019). We converted all statistics to r, using compute.es package in R (Del Re & Del Re, 2012) or when the package did not provide the needed functions we followed the formulas provided in Nakagawa & Cuthill (2007) and Koricheva, Gurevitch, & Mengersen (2013). Whenever study information was insufficient to compute the effect size, we contacted corresponding authors for missing data.

We converted our effect size r to Fisher’s Z in order to satisfy the normality assumption of parametric meta-analysis (Nakagawa & Cuthill, 2007). Fisher’s Z values were converted back to r, to ease interpretation of results.

Dataset

We collected a total of 481 effect sizes from 35 studies. These number was reduced to 364 effect sizes and 34 studies, after computing composite effect sizes between non-independent effect sizes and removing a study due to difficulty in describing the study design.

    df_raw <- read.csv("data/Table.S1.csv")
    n_used <- "n_adjusted"
    # Use n_adjusted as sample size
    df_tidy <- tidy_data(df_raw, n_used)
## Removed study id
##   54 
## Dataframe aggregated from 481 to 364 entries
    # Studies database
    studies <- read.csv("data/Table.S2.csv")
    df_tidy <- merge(df_tidy, studies, by.x = "id", by.y = "ID", all.x = TRUE)
    df_tidy <- df_tidy %>% 
               mutate(authors = paste(Authors, year)) %>%
               select(id, entry, authors, everything(), -Publ_year, -Title,
                      -doi, -Authors)

Table S1 - Complete dataset used in meta-analysis.

kable(df_tidy, format = "html") %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
     scroll_box(height = "400px", width = "100%")
id entry authors year impact_factor index taxa environ bio diversity_source pseudoreplication n z var Journal
2 15 Desjonquères et al. 2015 2015 2.180 ACI invertebrates A sound_abundance acoustic YES 4 0.3285000 0.7500000 PeerJ
2 16 Desjonquères et al. 2015 2015 2.180 ACI invertebrates A sound_richness acoustic YES 4 0.3322500 0.7500000 PeerJ
2 17 Desjonquères et al. 2015 2015 2.180 AR invertebrates A sound_abundance acoustic YES 4 0.0692000 0.7500000 PeerJ
2 18 Desjonquères et al. 2015 2015 2.180 AR invertebrates A sound_richness acoustic YES 4 0.0851500 0.7500000 PeerJ
2 19 Desjonquères et al. 2015 2015 2.180 Hf invertebrates A sound_abundance acoustic YES 4 -0.1851000 0.7500000 PeerJ
2 20 Desjonquères et al. 2015 2015 2.180 Hf invertebrates A sound_richness acoustic YES 4 -0.1304000 0.7500000 PeerJ
2 21 Desjonquères et al. 2015 2015 2.180 Ht invertebrates A sound_abundance acoustic YES 4 -0.3286500 0.7500000 PeerJ
2 22 Desjonquères et al. 2015 2015 2.180 Ht invertebrates A sound_richness acoustic YES 4 -0.2970000 0.7500000 PeerJ
2 23 Desjonquères et al. 2015 2015 2.180 M invertebrates A sound_abundance acoustic YES 4 0.3453000 0.7500000 PeerJ
2 24 Desjonquères et al. 2015 2015 2.180 M invertebrates A sound_richness acoustic YES 4 0.3043500 0.7500000 PeerJ
2 25 Desjonquères et al. 2015 2015 2.180 NP invertebrates A sound_abundance acoustic YES 4 0.2074500 0.7500000 PeerJ
2 26 Desjonquères et al. 2015 2015 2.180 NP invertebrates A sound_richness acoustic YES 4 0.1851000 0.7500000 PeerJ
4 13 Parks et al. 2014 2014 1.730 H mammals A sound_abundance acoustic YES 4 0.2801000 0.7500000 Ecol. Inform.
6 1 Boelman et al. 2007 2007 3.570 BIO birds T abundance no_acoustic NO 8 1.5047000 0.2000000 Ecol. Apli.
9 44 Harris et al. 2016 2016 5.710 ACI fish A diversity no_acoustic NO 9 1.0278500 0.1250250 Methods Ecol. Evol.
9 45 Harris et al. 2016 2016 5.710 ACI fish A richness no_acoustic NO 9 0.2877000 0.1667000 Methods Ecol. Evol.
9 46 Harris et al. 2016 2016 5.710 AR fish A diversity no_acoustic NO 9 0.2104000 0.1250250 Methods Ecol. Evol.
9 47 Harris et al. 2016 2016 5.710 AR fish A richness no_acoustic NO 9 0.5361000 0.1667000 Methods Ecol. Evol.
9 48 Harris et al. 2016 2016 5.710 H fish A diversity no_acoustic NO 9 0.3258625 0.0937688 Methods Ecol. Evol.
9 49 Harris et al. 2016 2016 5.710 H fish A richness no_acoustic NO 9 0.6448500 0.1041875 Methods Ecol. Evol.
10 38 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.1003000 0.0303000 Sci Rep
10 39 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.0000000 0.0303000 Sci Rep
10 170 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.8291000 0.0303000 Sci Rep
10 171 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.1717000 0.0303000 Sci Rep
10 228 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 1.2562000 0.0303000 Sci Rep
10 229 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.2132000 0.0303000 Sci Rep
10 276 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.9076000 0.0303000 Sci Rep
10 277 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.1923000 0.0303000 Sci Rep
10 300 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.1206000 0.0303000 Sci Rep
10 301 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.2554000 0.0303000 Sci Rep
10 311 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.0500000 0.0303000 Sci Rep
10 312 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.2769000 0.0303000 Sci Rep
10 326 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.3428000 0.0303000 Sci Rep
10 327 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.8673000 0.0303000 Sci Rep
10 339 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.3205000 0.0303000 Sci Rep
10 340 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 1.0454000 0.0303000 Sci Rep
10 350 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.3769000 0.0303000 Sci Rep
10 351 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 1.0203000 0.0303000 Sci Rep
10 361 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.2237000 0.0303000 Sci Rep
10 362 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.9287000 0.0303000 Sci Rep
10 363 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.3428000 0.0303000 Sci Rep
10 364 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.7582000 0.0303000 Sci Rep
11 40 Bertucci et al. 2016 2016 4.260 ACI fish A abundance no_acoustic NO 8 -0.1262000 0.1500000 Sci Rep
11 41 Bertucci et al. 2016 2016 4.260 ACI fish A diversity no_acoustic NO 8 1.0328000 0.2000000 Sci Rep
11 42 Bertucci et al. 2016 2016 4.260 ACI fish A richness no_acoustic NO 8 0.8821000 0.1500000 Sci Rep
11 172 Bertucci et al. 2016 2016 4.260 ACI fish A abundance no_acoustic NO 8 0.3009000 0.1500000 Sci Rep
11 173 Bertucci et al. 2016 2016 4.260 ACI fish A diversity no_acoustic NO 8 0.4181000 0.2000000 Sci Rep
11 174 Bertucci et al. 2016 2016 4.260 ACI fish A richness no_acoustic NO 8 1.1245500 0.1500000 Sci Rep
11 230 Bertucci et al. 2016 2016 4.260 ACI fish A abundance no_acoustic NO 8 -0.1797000 0.1500000 Sci Rep
11 231 Bertucci et al. 2016 2016 4.260 ACI fish A diversity no_acoustic NO 8 0.8429000 0.2000000 Sci Rep
11 232 Bertucci et al. 2016 2016 4.260 ACI fish A richness no_acoustic NO 8 0.6372500 0.1500000 Sci Rep
11 278 Bertucci et al. 2016 2016 4.260 ACI fish A abundance no_acoustic NO 8 -0.0471500 0.1500000 Sci Rep
11 279 Bertucci et al. 2016 2016 4.260 ACI fish A diversity no_acoustic NO 8 0.6747000 0.2000000 Sci Rep
11 280 Bertucci et al. 2016 2016 4.260 ACI fish A richness no_acoustic NO 8 0.6980000 0.1500000 Sci Rep
13 11 McWilliam & Hawkin 2013 2013 2.480 ACI invertebrates A sound_abundance acoustic YES 5 1.1881000 0.5000000 J. ExpMar. Biol. Ecol
13 12 McWilliam & Hawkin 2013 2013 2.480 ADI invertebrates A sound_abundance acoustic YES 5 1.3331000 0.5000000 J. ExpMar. Biol. Ecol
14 34 Wa Maina et al. 2016 2016 1.220 ACI birds T richness acoustic NO 8 0.1689000 0.2000000 BDJ
14 35 Wa Maina et al. 2016 2016 1.220 ACI birds T richness no_acoustic NO 8 0.9638000 0.2000000 BDJ
14 36 Wa Maina et al. 2016 2016 1.220 H birds T richness acoustic NO 8 0.4676000 0.2000000 BDJ
14 37 Wa Maina et al. 2016 2016 1.220 H birds T richness no_acoustic NO 8 0.5870000 0.2000000 BDJ
15 43 Roca & Proulx 2016 2016 4.810 H invertebrates T richness acoustic NO 4 1.8972000 1.0000000 Ecology
15 175 Roca & Proulx 2016 2016 4.810 H invertebrates T richness acoustic NO 4 2.6467000 1.0000000 Ecology
15 233 Roca & Proulx 2016 2016 4.810 H invertebrates T richness acoustic NO 4 2.3796000 1.0000000 Ecology
17 14 Zhang et al. 2015 2015 0.000 ACI birds T richness acoustic YES 4 0.3940000 1.0000000 IEEE.Conference.procedings
37 33 Picciulin et al. 2016 2016 0.000 ACI fish A sound_abundance acoustic YES 4 0.3260000 1.0000000 Proceedings of Meetings on Acoustics
41 3 Paisley-Jones 2011 2011 0.000 H birds T sound_abundance acoustic NO 6 0.1481000 0.3333000 thesis
41 4 Paisley-Jones 2011 2011 0.000 H invertebrates T diversity no_acoustic NO 6 -0.1430000 0.3333000 thesis
44 86 Machado et al. 2017 2017 4.994 ADI birds T richness acoustic NO 30 0.4910000 0.0370000 Landsc. Urban Plan.
44 87 Machado et al. 2017 2017 4.994 NDSI birds T richness acoustic NO 30 0.1686000 0.0370000 Landsc. Urban Plan.
45 7 McLaren 2012 2012 0.000 NDSI birds T diversity no_acoustic NO 36 0.9330000 0.0303000 practicum
45 8 McLaren 2012 2012 0.000 NDSI birds T richness acoustic NO 36 0.6070000 0.0303000 practicum
45 9 McLaren 2012 2012 0.000 NDSI birds T richness no_acoustic NO 36 0.9160000 0.0303000 practicum
53 154 Moreno-Gomez 2019 2019 4.490 ACI anurans T richness acoustic NO 33 0.0000000 0.0333000 Ecol. Indic.
53 155 Moreno-Gomez 2019 2019 4.490 ACI birds T richness acoustic NO 33 -0.0209000 0.0333000 Ecol. Indic.
53 156 Moreno-Gomez 2019 2019 4.490 ADI anurans T richness acoustic NO 33 0.1051000 0.0333000 Ecol. Indic.
53 157 Moreno-Gomez 2019 2019 4.490 ADI birds T richness acoustic NO 33 -0.3237000 0.0333000 Ecol. Indic.
53 158 Moreno-Gomez 2019 2019 4.490 AEI anurans T richness acoustic NO 33 -0.2122000 0.0333000 Ecol. Indic.
53 159 Moreno-Gomez 2019 2019 4.490 AEI birds T richness acoustic NO 33 0.3237000 0.0333000 Ecol. Indic.
53 160 Moreno-Gomez 2019 2019 4.490 BIO anurans T richness acoustic NO 33 0.1051000 0.0333000 Ecol. Indic.
53 161 Moreno-Gomez 2019 2019 4.490 BIO birds T richness acoustic NO 33 -0.1051000 0.0333000 Ecol. Indic.
53 162 Moreno-Gomez 2019 2019 4.490 H anurans T richness acoustic NO 33 0.1051000 0.0333000 Ecol. Indic.
53 163 Moreno-Gomez 2019 2019 4.490 H birds T richness acoustic NO 33 -0.1051000 0.0333000 Ecol. Indic.
53 164 Moreno-Gomez 2019 2019 4.490 Hf anurans T richness acoustic NO 33 -0.1051000 0.0333000 Ecol. Indic.
53 165 Moreno-Gomez 2019 2019 4.490 Hf birds T richness acoustic NO 33 0.1051000 0.0333000 Ecol. Indic.
53 166 Moreno-Gomez 2019 2019 4.490 Ht anurans T richness acoustic NO 33 0.1051000 0.0333000 Ecol. Indic.
53 167 Moreno-Gomez 2019 2019 4.490 Ht birds T richness acoustic NO 33 -0.3237000 0.0333000 Ecol. Indic.
53 214 Moreno-Gomez 2019 2019 4.490 ACI anurans T richness acoustic NO 11 -0.1051000 0.1250000 Ecol. Indic.
53 215 Moreno-Gomez 2019 2019 4.490 ACI birds T richness acoustic NO 11 0.3237000 0.1250000 Ecol. Indic.
53 216 Moreno-Gomez 2019 2019 4.490 ADI anurans T richness acoustic NO 11 0.1051000 0.1250000 Ecol. Indic.
53 217 Moreno-Gomez 2019 2019 4.490 ADI birds T richness acoustic NO 11 -0.2122000 0.1250000 Ecol. Indic.
53 218 Moreno-Gomez 2019 2019 4.490 AEI anurans T richness acoustic NO 11 -0.1051000 0.1250000 Ecol. Indic.
53 219 Moreno-Gomez 2019 2019 4.490 AEI birds T richness acoustic NO 11 0.3237000 0.1250000 Ecol. Indic.
53 220 Moreno-Gomez 2019 2019 4.490 BIO anurans T richness acoustic NO 11 -0.1051000 0.1250000 Ecol. Indic.
53 221 Moreno-Gomez 2019 2019 4.490 BIO birds T richness acoustic NO 11 0.2122000 0.1250000 Ecol. Indic.
53 222 Moreno-Gomez 2019 2019 4.490 H anurans T richness acoustic NO 11 0.1051000 0.1250000 Ecol. Indic.
53 223 Moreno-Gomez 2019 2019 4.490 H birds T richness acoustic NO 11 -0.3237000 0.1250000 Ecol. Indic.
53 224 Moreno-Gomez 2019 2019 4.490 Hf anurans T richness acoustic NO 11 -0.0105000 0.1250000 Ecol. Indic.
53 225 Moreno-Gomez 2019 2019 4.490 Hf birds T richness acoustic NO 11 -0.1051000 0.1250000 Ecol. Indic.
53 226 Moreno-Gomez 2019 2019 4.490 Ht anurans T richness acoustic NO 11 0.1051000 0.1250000 Ecol. Indic.
53 227 Moreno-Gomez 2019 2019 4.490 Ht birds T richness acoustic NO 11 -0.4426000 0.1250000 Ecol. Indic.
53 262 Moreno-Gomez 2019 2019 4.490 ACI anurans T richness acoustic NO 32 0.1051000 0.0345000 Ecol. Indic.
53 263 Moreno-Gomez 2019 2019 4.490 ACI birds T richness acoustic NO 32 0.5731000 0.0345000 Ecol. Indic.
53 264 Moreno-Gomez 2019 2019 4.490 ADI anurans T richness acoustic NO 32 0.1051000 0.0345000 Ecol. Indic.
53 265 Moreno-Gomez 2019 2019 4.490 ADI birds T richness acoustic NO 32 -0.1051000 0.0345000 Ecol. Indic.
53 266 Moreno-Gomez 2019 2019 4.490 AEI anurans T richness acoustic NO 32 -0.1051000 0.0345000 Ecol. Indic.
53 267 Moreno-Gomez 2019 2019 4.490 AEI birds T richness acoustic NO 32 0.2122000 0.0345000 Ecol. Indic.
53 268 Moreno-Gomez 2019 2019 4.490 BIO anurans T richness acoustic NO 32 -0.2122000 0.0345000 Ecol. Indic.
53 269 Moreno-Gomez 2019 2019 4.490 BIO birds T richness acoustic NO 32 0.3237000 0.0345000 Ecol. Indic.
53 270 Moreno-Gomez 2019 2019 4.490 H anurans T richness acoustic NO 32 0.0105000 0.0345000 Ecol. Indic.
53 271 Moreno-Gomez 2019 2019 4.490 H birds T richness acoustic NO 32 0.3237000 0.0345000 Ecol. Indic.
53 272 Moreno-Gomez 2019 2019 4.490 Hf anurans T richness acoustic NO 32 0.0000000 0.0345000 Ecol. Indic.
53 273 Moreno-Gomez 2019 2019 4.490 Hf birds T richness acoustic NO 32 0.3237000 0.0345000 Ecol. Indic.
53 274 Moreno-Gomez 2019 2019 4.490 Ht anurans T richness acoustic NO 32 0.0105000 0.0345000 Ecol. Indic.
53 275 Moreno-Gomez 2019 2019 4.490 Ht birds T richness acoustic NO 32 -0.4426000 0.0345000 Ecol. Indic.
60 152 Patrick Lyon et al. 2019 2019 2.360 ACI fish A abundance no_acoustic NO 7 -0.3654000 0.2500000 Mar. Ecol.-Prog. Ser.
60 153 Patrick Lyon et al. 2019 2019 2.360 ACI fish A diversity no_acoustic NO 7 0.1306000 0.1875000 Mar. Ecol.-Prog. Ser.
60 212 Patrick Lyon et al. 2019 2019 2.360 ACI fish A abundance no_acoustic NO 7 0.5763000 0.2500000 Mar. Ecol.-Prog. Ser.
60 213 Patrick Lyon et al. 2019 2019 2.360 ACI fish A diversity no_acoustic NO 7 0.4313500 0.1875000 Mar. Ecol.-Prog. Ser.
70 94 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic NO 9 -0.2232000 0.1250250 Sci Rep
70 95 Bolgan et al. 2018 2018 4.010 ACI fish A sound_richness acoustic NO 9 0.6011000 0.1667000 Sci Rep
70 199 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic NO 4 0.0262000 1.0000000 Sci Rep
70 200 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic NO 9 0.6011000 0.1667000 Sci Rep
70 201 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic NO 10 0.4676000 0.1429000 Sci Rep
70 202 Bolgan et al. 2018 2018 4.010 ACI fish A sound_richness acoustic NO 9 0.1051000 0.1667000 Sci Rep
70 203 Bolgan et al. 2018 2018 4.010 ACI fish A sound_richness acoustic NO 10 0.4931000 0.1429000 Sci Rep
70 256 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic NO 9 0.8244000 0.1667000 Sci Rep
70 257 Bolgan et al. 2018 2018 4.010 ACI fish A sound_richness acoustic NO 9 1.2973000 0.1667000 Sci Rep
70 294 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic YES 25 0.3422714 0.0260000 Sci Rep
70 295 Bolgan et al. 2018 2018 4.010 ACI fish A sound_richness acoustic YES 25 0.2930143 0.0260000 Sci Rep
77 71 Fairbrass et al. 2017 2017 3.980 ACI several T richness acoustic NO 105 0.8513000 0.0099000 Ecol. Indic.
77 80 Fairbrass et al. 2017 2017 3.980 BIO several T richness acoustic NO 105 0.4545000 0.0099000 Ecol. Indic.
77 85 Fairbrass et al. 2017 2017 3.980 NDSI several T richness acoustic NO 105 0.4320000 0.0099000 Ecol. Indic.
77 186 Fairbrass et al. 2017 2017 3.980 ADI several T richness acoustic NO 105 0.1968000 0.0099000 Ecol. Indic.
80 62 Staaterman et al. 2017 2017 2.280 ACI fish A abundance no_acoustic NO 4 -0.1203000 0.6666667 Mar. Ecol.-Prog. Ser.
80 63 Staaterman et al. 2017 2017 2.280 ACI fish A diversity no_acoustic NO 4 0.0000000 0.7500000 Mar. Ecol.-Prog. Ser.
80 64 Staaterman et al. 2017 2017 2.280 ACI fish A richness no_acoustic NO 4 -0.2158000 0.6666667 Mar. Ecol.-Prog. Ser.
80 65 Staaterman et al. 2017 2017 2.280 H fish A abundance no_acoustic NO 4 0.4060667 0.6666667 Mar. Ecol.-Prog. Ser.
80 66 Staaterman et al. 2017 2017 2.280 H fish A diversity no_acoustic NO 4 -0.1586500 0.7500000 Mar. Ecol.-Prog. Ser.
80 67 Staaterman et al. 2017 2017 2.280 H fish A richness no_acoustic NO 4 0.3178667 0.6666667 Mar. Ecol.-Prog. Ser.
80 176 Staaterman et al. 2017 2017 2.280 ACI fish A abundance no_acoustic NO 4 0.0335667 0.6666667 Mar. Ecol.-Prog. Ser.
80 177 Staaterman et al. 2017 2017 2.280 ACI fish A diversity no_acoustic NO 4 0.1990500 0.7500000 Mar. Ecol.-Prog. Ser.
80 178 Staaterman et al. 2017 2017 2.280 ACI fish A richness no_acoustic NO 4 0.0357000 0.6666667 Mar. Ecol.-Prog. Ser.
80 179 Staaterman et al. 2017 2017 2.280 H fish A abundance no_acoustic NO 4 0.4564000 0.6666667 Mar. Ecol.-Prog. Ser.
80 180 Staaterman et al. 2017 2017 2.280 H fish A diversity no_acoustic NO 4 -0.2144000 0.7500000 Mar. Ecol.-Prog. Ser.
80 181 Staaterman et al. 2017 2017 2.280 H fish A richness no_acoustic NO 4 0.4316333 0.6666667 Mar. Ecol.-Prog. Ser.
80 234 Staaterman et al. 2017 2017 2.280 ACI fish A abundance no_acoustic NO 4 0.0357000 0.6666667 Mar. Ecol.-Prog. Ser.
80 235 Staaterman et al. 2017 2017 2.280 ACI fish A diversity no_acoustic NO 4 -0.2144000 0.7500000 Mar. Ecol.-Prog. Ser.
80 236 Staaterman et al. 2017 2017 2.280 ACI fish A richness no_acoustic NO 4 -0.3736000 0.6666667 Mar. Ecol.-Prog. Ser.
80 237 Staaterman et al. 2017 2017 2.280 H fish A abundance no_acoustic NO 4 0.2231333 0.6666667 Mar. Ecol.-Prog. Ser.
80 238 Staaterman et al. 2017 2017 2.280 H fish A diversity no_acoustic NO 4 0.1093000 0.7500000 Mar. Ecol.-Prog. Ser.
80 239 Staaterman et al. 2017 2017 2.280 H fish A richness no_acoustic NO 4 0.0417667 0.6666667 Mar. Ecol.-Prog. Ser.
80 281 Staaterman et al. 2017 2017 2.280 ACI fish A abundance no_acoustic NO 4 0.0357000 0.6666667 Mar. Ecol.-Prog. Ser.
80 282 Staaterman et al. 2017 2017 2.280 ACI fish A diversity no_acoustic NO 4 -0.1586500 0.7500000 Mar. Ecol.-Prog. Ser.
80 283 Staaterman et al. 2017 2017 2.280 ACI fish A richness no_acoustic NO 4 -0.3385667 0.6666667 Mar. Ecol.-Prog. Ser.
80 284 Staaterman et al. 2017 2017 2.280 H fish A abundance no_acoustic NO 4 0.2231333 0.6666667 Mar. Ecol.-Prog. Ser.
80 285 Staaterman et al. 2017 2017 2.280 H fish A diversity no_acoustic NO 4 -0.1061000 0.7500000 Mar. Ecol.-Prog. Ser.
80 286 Staaterman et al. 2017 2017 2.280 H fish A richness no_acoustic NO 4 0.2055667 0.6666667 Mar. Ecol.-Prog. Ser.
86 92 Indraswari et al. 2018 2018 3.400 ACI anurans T sound_abundance acoustic YES 33 0.6284000 0.0333000 Freshw. Biol.
86 93 Indraswari et al. 2018 2018 3.400 Ht anurans T sound_abundance acoustic YES 33 0.6948000 0.0333000 Freshw. Biol.
86 197 Indraswari et al. 2018 2018 3.400 ACI anurans T sound_abundance acoustic YES 33 0.3773000 0.0333000 Freshw. Biol.
86 198 Indraswari et al. 2018 2018 3.400 Ht anurans T sound_abundance acoustic YES 33 0.4426000 0.0333000 Freshw. Biol.
86 254 Indraswari et al. 2018 2018 3.400 ACI anurans T sound_abundance acoustic YES 33 0.2617000 0.0333000 Freshw. Biol.
86 255 Indraswari et al. 2018 2018 3.400 Ht anurans T sound_abundance acoustic YES 33 0.4880000 0.0333000 Freshw. Biol.
87 96 Eldridge et al. 2018 2018 4.490 ACI birds T richness acoustic YES 4 0.6250000 0.6000000 Ecol. Indic.
87 98 Eldridge et al. 2018 2018 4.490 ACI birds T sound_abundance acoustic YES 4 0.6507600 0.6000000 Ecol. Indic.
87 204 Eldridge et al. 2018 2018 4.490 ACI birds T richness acoustic YES 4 0.1909000 0.6000000 Ecol. Indic.
87 206 Eldridge et al. 2018 2018 4.490 ACI several T sound_abundance acoustic YES 4 -0.0819200 0.6000000 Ecol. Indic.
87 258 Eldridge et al. 2018 2018 4.490 ADI birds T richness acoustic YES 4 -0.7218000 1.0000000 Ecol. Indic.
87 259 Eldridge et al. 2018 2018 4.490 ADI birds T sound_abundance acoustic YES 4 -0.7547000 1.0000000 Ecol. Indic.
87 260 Eldridge et al. 2018 2018 4.490 AEI birds T richness acoustic YES 4 0.5061000 1.0000000 Ecol. Indic.
87 261 Eldridge et al. 2018 2018 4.490 AEI birds T sound_abundance acoustic YES 4 0.8429000 1.0000000 Ecol. Indic.
87 296 Eldridge et al. 2018 2018 4.490 ADI birds T richness acoustic YES 4 -0.2078000 1.0000000 Ecol. Indic.
87 297 Eldridge et al. 2018 2018 4.490 ADI several T sound_abundance acoustic YES 4 -0.5061000 1.0000000 Ecol. Indic.
87 298 Eldridge et al. 2018 2018 4.490 AEI birds T richness acoustic YES 4 0.2100000 1.0000000 Ecol. Indic.
87 299 Eldridge et al. 2018 2018 4.490 AEI several T sound_abundance acoustic YES 4 0.5731000 1.0000000 Ecol. Indic.
87 309 Eldridge et al. 2018 2018 4.490 BIO birds T richness acoustic YES 4 0.8064000 1.0000000 Ecol. Indic.
87 310 Eldridge et al. 2018 2018 4.490 BIO birds T sound_abundance acoustic YES 4 0.9009000 1.0000000 Ecol. Indic.
87 320 Eldridge et al. 2018 2018 4.490 BIO birds T richness acoustic YES 4 0.2100000 1.0000000 Ecol. Indic.
87 321 Eldridge et al. 2018 2018 4.490 BIO several T sound_abundance acoustic YES 4 0.6446000 1.0000000 Ecol. Indic.
87 322 Eldridge et al. 2018 2018 4.490 Hf birds T richness acoustic YES 4 -0.5731000 1.0000000 Ecol. Indic.
87 323 Eldridge et al. 2018 2018 4.490 Hf birds T sound_abundance acoustic YES 4 -0.7218000 1.0000000 Ecol. Indic.
87 324 Eldridge et al. 2018 2018 4.490 Ht birds T richness acoustic YES 4 -0.6595000 1.0000000 Ecol. Indic.
87 325 Eldridge et al. 2018 2018 4.490 Ht birds T sound_abundance acoustic YES 4 -0.6902000 1.0000000 Ecol. Indic.
87 335 Eldridge et al. 2018 2018 4.490 Hf birds T richness acoustic YES 4 -0.1905000 1.0000000 Ecol. Indic.
87 336 Eldridge et al. 2018 2018 4.490 Hf several T sound_abundance acoustic YES 4 -0.6446000 1.0000000 Ecol. Indic.
87 337 Eldridge et al. 2018 2018 4.490 Ht birds T richness acoustic YES 4 -0.1905000 1.0000000 Ecol. Indic.
87 338 Eldridge et al. 2018 2018 4.490 Ht several T sound_abundance acoustic YES 4 -0.3237000 1.0000000 Ecol. Indic.
87 348 Eldridge et al. 2018 2018 4.490 NDSI birds T richness acoustic YES 4 0.3237000 1.0000000 Ecol. Indic.
87 349 Eldridge et al. 2018 2018 4.490 NDSI birds T sound_abundance acoustic YES 4 0.4303000 1.0000000 Ecol. Indic.
87 359 Eldridge et al. 2018 2018 4.490 NDSI birds T richness acoustic YES 4 0.2672000 1.0000000 Ecol. Indic.
87 360 Eldridge et al. 2018 2018 4.490 NDSI several T sound_abundance acoustic YES 4 0.4303000 1.0000000 Ecol. Indic.
89 50 Gage et al. 2017 2017 1.820 ACI birds T richness acoustic YES 60 0.6885000 0.0175000 Ecol. Inform.
89 51 Gage et al. 2017 2017 1.820 ACI birds T sound_abundance acoustic YES 60 1.4618000 0.0175000 Ecol. Inform.
89 52 Gage et al. 2017 2017 1.820 ADI birds T richness acoustic YES 60 -1.8635000 0.0175000 Ecol. Inform.
89 53 Gage et al. 2017 2017 1.820 ADI birds T sound_abundance acoustic YES 60 -0.8852000 0.0175000 Ecol. Inform.
89 54 Gage et al. 2017 2017 1.820 AEI birds T richness acoustic YES 60 2.2494000 0.0175000 Ecol. Inform.
89 55 Gage et al. 2017 2017 1.820 AEI birds T sound_abundance acoustic YES 60 1.0302000 0.0175000 Ecol. Inform.
89 56 Gage et al. 2017 2017 1.820 BIO birds T richness acoustic YES 60 -0.6098000 0.0175000 Ecol. Inform.
89 57 Gage et al. 2017 2017 1.820 BIO birds T sound_abundance acoustic YES 60 -0.0993000 0.0175000 Ecol. Inform.
89 58 Gage et al. 2017 2017 1.820 H birds T richness acoustic YES 60 1.0849000 0.0175000 Ecol. Inform.
89 59 Gage et al. 2017 2017 1.820 H birds T sound_abundance acoustic YES 60 2.4427000 0.0175000 Ecol. Inform.
89 60 Gage et al. 2017 2017 1.820 NDSI birds T richness acoustic YES 60 0.0270000 0.0175000 Ecol. Inform.
89 61 Gage et al. 2017 2017 1.820 NDSI birds T sound_abundance acoustic YES 60 0.5269000 0.0175000 Ecol. Inform.
90 104 Ferreira et al. 2018 2018 NA ACI anurans T sound_richness acoustic NO 7 0.0556000 0.2500000 Journal of ecoacoustics
90 108 Ferreira et al. 2018 2018 NA ACI birds T sound_richness acoustic NO 7 -0.1125000 0.2500000 Journal of ecoacoustics
90 109 Ferreira et al. 2018 2018 NA ACI invertebrates T sound_richness acoustic NO 7 0.1765000 0.2500000 Journal of ecoacoustics
90 110 Ferreira et al. 2018 2018 NA ACI mammals T sound_richness acoustic NO 7 -0.0598000 0.2500000 Journal of ecoacoustics
90 111 Ferreira et al. 2018 2018 NA ADI anurans T sound_richness acoustic NO 7 1.0277000 0.2500000 Journal of ecoacoustics
90 115 Ferreira et al. 2018 2018 NA ADI birds T sound_richness acoustic NO 7 -0.4747000 0.2500000 Journal of ecoacoustics
90 116 Ferreira et al. 2018 2018 NA ADI invertebrates T sound_richness acoustic NO 7 0.8162000 0.2500000 Journal of ecoacoustics
90 117 Ferreira et al. 2018 2018 NA ADI mammals T sound_richness acoustic NO 7 0.3826000 0.2500000 Journal of ecoacoustics
90 118 Ferreira et al. 2018 2018 NA AEI anurans T sound_richness acoustic NO 7 -0.9660000 0.2500000 Journal of ecoacoustics
90 122 Ferreira et al. 2018 2018 NA AEI birds T sound_richness acoustic NO 7 0.6011000 0.2500000 Journal of ecoacoustics
90 123 Ferreira et al. 2018 2018 NA AEI invertebrates T sound_richness acoustic NO 7 -0.7058000 0.2500000 Journal of ecoacoustics
90 124 Ferreira et al. 2018 2018 NA AEI mammals T sound_richness acoustic NO 7 -0.4389000 0.2500000 Journal of ecoacoustics
90 125 Ferreira et al. 2018 2018 NA BIO anurans T sound_richness acoustic NO 7 -0.1743000 0.2500000 Journal of ecoacoustics
90 129 Ferreira et al. 2018 2018 NA BIO birds T sound_richness acoustic NO 7 -0.1358000 0.2500000 Journal of ecoacoustics
90 130 Ferreira et al. 2018 2018 NA BIO invertebrates T sound_richness acoustic NO 7 -0.0556000 0.2500000 Journal of ecoacoustics
90 131 Ferreira et al. 2018 2018 NA BIO mammals T sound_richness acoustic NO 7 -0.0839000 0.2500000 Journal of ecoacoustics
90 132 Ferreira et al. 2018 2018 NA H anurans T sound_richness acoustic NO 7 1.2090000 0.2500000 Journal of ecoacoustics
90 136 Ferreira et al. 2018 2018 NA H birds T sound_richness acoustic NO 7 -0.5048000 0.2500000 Journal of ecoacoustics
90 137 Ferreira et al. 2018 2018 NA H invertebrates T sound_richness acoustic NO 7 0.8949000 0.2500000 Journal of ecoacoustics
90 138 Ferreira et al. 2018 2018 NA H mammals T sound_richness acoustic NO 7 0.4169000 0.2500000 Journal of ecoacoustics
90 142 Ferreira et al. 2018 2018 NA NDSI anurans T sound_richness acoustic NO 7 1.1933000 0.2500000 Journal of ecoacoustics
90 146 Ferreira et al. 2018 2018 NA NDSI birds T sound_richness acoustic NO 7 -0.5567000 0.2500000 Journal of ecoacoustics
90 147 Ferreira et al. 2018 2018 NA NDSI invertebrates T sound_richness acoustic NO 7 0.8485000 0.2500000 Journal of ecoacoustics
90 148 Ferreira et al. 2018 2018 NA NDSI mammals T sound_richness acoustic NO 7 0.4513000 0.2500000 Journal of ecoacoustics
92 88 Torti et al. 2018 2018 1.950 ACI mammals T abundance no_acoustic NO 258 0.6150000 0.0039000
92 89 Torti et al. 2018 2018 1.950 ADI mammals T abundance no_acoustic NO 258 0.0174000 0.0039000
92 90 Torti et al. 2018 2018 1.950 AR mammals T abundance no_acoustic NO 258 0.0266000 0.0039000
92 91 Torti et al. 2018 2018 1.950 H mammals T abundance no_acoustic NO 258 0.1404000 0.0039000
96 105 Izaguirre et al.  2018 2018 NA ACI birds T abundance no_acoustic YES 12 0.7315000 0.1111000 Journal of ecoacoustics
96 106 Izaguirre et al.  2018 2018 NA ACI birds T diversity no_acoustic YES 12 -0.7068500 0.0833250 Journal of ecoacoustics
96 107 Izaguirre et al.  2018 2018 NA ACI birds T richness no_acoustic YES 12 -0.7941000 0.1111000 Journal of ecoacoustics
96 112 Izaguirre et al.  2018 2018 NA ADI birds T abundance no_acoustic YES 12 -0.7958000 0.1111000 Journal of ecoacoustics
96 113 Izaguirre et al.  2018 2018 NA ADI birds T diversity no_acoustic YES 12 0.6084000 0.0833250 Journal of ecoacoustics
96 114 Izaguirre et al.  2018 2018 NA ADI birds T richness no_acoustic YES 12 0.5192000 0.1111000 Journal of ecoacoustics
96 119 Izaguirre et al.  2018 2018 NA AEI birds T abundance no_acoustic YES 12 0.7430000 0.1111000 Journal of ecoacoustics
96 120 Izaguirre et al.  2018 2018 NA AEI birds T diversity no_acoustic YES 12 -0.5394000 0.0833250 Journal of ecoacoustics
96 121 Izaguirre et al.  2018 2018 NA AEI birds T richness no_acoustic YES 12 -0.4181000 0.1111000 Journal of ecoacoustics
96 126 Izaguirre et al.  2018 2018 NA BIO birds T abundance no_acoustic YES 12 0.5553000 0.1111000 Journal of ecoacoustics
96 127 Izaguirre et al.  2018 2018 NA BIO birds T diversity no_acoustic YES 12 -0.6801500 0.0833250 Journal of ecoacoustics
96 128 Izaguirre et al.  2018 2018 NA BIO birds T richness no_acoustic YES 12 -0.5485000 0.1111000 Journal of ecoacoustics
96 133 Izaguirre et al.  2018 2018 NA H birds T abundance no_acoustic YES 12 -0.3904000 0.1111000 Journal of ecoacoustics
96 134 Izaguirre et al.  2018 2018 NA H birds T diversity no_acoustic YES 12 0.3747000 0.0833250 Journal of ecoacoustics
96 135 Izaguirre et al.  2018 2018 NA H birds T richness no_acoustic YES 12 0.5773000 0.1111000 Journal of ecoacoustics
96 139 Izaguirre et al.  2018 2018 NA M birds T abundance no_acoustic YES 12 0.4944000 0.1111000 Journal of ecoacoustics
96 140 Izaguirre et al.  2018 2018 NA M birds T diversity no_acoustic YES 12 -0.6604000 0.0833250 Journal of ecoacoustics
96 141 Izaguirre et al.  2018 2018 NA M birds T richness no_acoustic YES 12 -0.6686000 0.1111000 Journal of ecoacoustics
96 143 Izaguirre et al.  2018 2018 NA NDSI birds T abundance no_acoustic YES 12 -0.0955000 0.1111000 Journal of ecoacoustics
96 144 Izaguirre et al.  2018 2018 NA NDSI birds T diversity no_acoustic YES 12 0.2976500 0.0833250 Journal of ecoacoustics
96 145 Izaguirre et al.  2018 2018 NA NDSI birds T richness no_acoustic YES 12 0.4012000 0.1111000 Journal of ecoacoustics
96 149 Izaguirre et al.  2018 2018 NA NP birds T abundance no_acoustic YES 12 -0.5983000 0.1111000 Journal of ecoacoustics
96 150 Izaguirre et al.  2018 2018 NA NP birds T diversity no_acoustic YES 12 0.6247500 0.0833250 Journal of ecoacoustics
96 151 Izaguirre et al.  2018 2018 NA NP birds T richness no_acoustic YES 12 0.6372000 0.1111000 Journal of ecoacoustics
251 168 Buxton et al. 2016 2016 2.440 ACI birds T diversity acoustic NO 72 0.2300000 0.0108750 Ecol. Evol.
251 169 Buxton et al. 2016 2016 2.440 ACI birds T richness acoustic NO 72 0.3673000 0.0145000 Ecol. Evol.
427 27 Fuller et al. 2015 2015 3.190 ACI birds T richness acoustic NO 380 0.0503000 0.0027000 Ecol. Indic.
427 28 Fuller et al. 2015 2015 3.190 ADI birds T richness acoustic NO 380 0.0395000 0.0027000 Ecol. Indic.
427 29 Fuller et al. 2015 2015 3.190 AEI birds T richness acoustic NO 380 0.0864000 0.0027000 Ecol. Indic.
427 30 Fuller et al. 2015 2015 3.190 BIO birds T richness acoustic NO 380 0.0543000 0.0027000 Ecol. Indic.
427 31 Fuller et al. 2015 2015 3.190 H birds T richness acoustic NO 380 0.1281000 0.0027000 Ecol. Indic.
427 32 Fuller et al. 2015 2015 3.190 NDSI birds T richness acoustic NO 380 0.1517000 0.0027000 Ecol. Indic.
1132 10 Depraetere et al. 2012 2012 2.890 AR birds T richness acoustic NO 12 1.7295000 0.1111000 Ecol. Indic.
1177 5 Joo et al. 2011 2011 2.170 H birds T richness acoustic YES 5 0.1820000 0.5000000 Landsc. Urban Plan.
1262 6 Pieretti et al. 2011 2011 2.700 ACI birds T sound_abundance acoustic YES 20 1.2568600 0.0352800 Ecol. Indic.
2740 69 Mammides et al. 2017 2017 3.980 ACI birds T diversity no_acoustic NO 47 0.1614000 0.0227000 Ecol. Indic.
2740 70 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 47 0.2132000 0.0227000 Ecol. Indic.
2740 72 Mammides et al. 2017 2017 3.980 ADI birds T diversity no_acoustic NO 47 0.3769000 0.0227000 Ecol. Indic.
2740 73 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 47 0.3654000 0.0227000 Ecol. Indic.
2740 74 Mammides et al. 2017 2017 3.980 AEI birds T diversity no_acoustic NO 47 -0.4118000 0.0227000 Ecol. Indic.
2740 75 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 47 -0.4236000 0.0227000 Ecol. Indic.
2740 76 Mammides et al. 2017 2017 3.980 AR birds T diversity no_acoustic NO 47 -0.4847000 0.0227000 Ecol. Indic.
2740 77 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 47 -0.4973000 0.0227000 Ecol. Indic.
2740 78 Mammides et al. 2017 2017 3.980 BIO birds T diversity no_acoustic NO 47 -0.2986000 0.0227000 Ecol. Indic.
2740 79 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 47 -0.3541000 0.0227000 Ecol. Indic.
2740 81 Mammides et al. 2017 2017 3.980 H birds T diversity no_acoustic NO 47 0.5361000 0.0227000 Ecol. Indic.
2740 82 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 47 0.5627000 0.0227000 Ecol. Indic.
2740 83 Mammides et al. 2017 2017 3.980 NDSI birds T diversity no_acoustic NO 47 -0.0100000 0.0227000 Ecol. Indic.
2740 84 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 47 -0.0100000 0.0227000 Ecol. Indic.
2740 182 Mammides et al. 2017 2017 3.980 ACI birds T diversity no_acoustic NO 47 0.0601000 0.0227000 Ecol. Indic.
2740 183 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 47 -0.0300000 0.0227000 Ecol. Indic.
2740 184 Mammides et al. 2017 2017 3.980 ADI birds T diversity no_acoustic NO 47 0.7250000 0.0227000 Ecol. Indic.
2740 185 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 47 0.6184000 0.0227000 Ecol. Indic.
2740 187 Mammides et al. 2017 2017 3.980 AEI birds T diversity no_acoustic NO 47 -0.7089000 0.0227000 Ecol. Indic.
2740 188 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 47 -0.6042000 0.0227000 Ecol. Indic.
2740 189 Mammides et al. 2017 2017 3.980 AR birds T diversity no_acoustic NO 47 -0.2448000 0.0227000 Ecol. Indic.
2740 190 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 47 -0.2448000 0.0227000 Ecol. Indic.
2740 191 Mammides et al. 2017 2017 3.980 BIO birds T diversity no_acoustic NO 47 0.2027000 0.0227000 Ecol. Indic.
2740 192 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 47 0.2342000 0.0227000 Ecol. Indic.
2740 193 Mammides et al. 2017 2017 3.980 H birds T diversity no_acoustic NO 47 0.7928000 0.0227000 Ecol. Indic.
2740 194 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 47 0.6777000 0.0227000 Ecol. Indic.
2740 195 Mammides et al. 2017 2017 3.980 NDSI birds T diversity no_acoustic NO 47 0.6931000 0.0227000 Ecol. Indic.
2740 196 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 47 0.6042000 0.0227000 Ecol. Indic.
2740 240 Mammides et al. 2017 2017 3.980 ACI birds T diversity no_acoustic NO 50 0.0601000 0.0213000 Ecol. Indic.
2740 241 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 50 0.0400000 0.0213000 Ecol. Indic.
2740 242 Mammides et al. 2017 2017 3.980 ADI birds T diversity no_acoustic NO 50 0.5101000 0.0213000 Ecol. Indic.
2740 243 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 50 0.6328000 0.0213000 Ecol. Indic.
2740 244 Mammides et al. 2017 2017 3.980 AEI birds T diversity no_acoustic NO 50 -0.4973000 0.0213000 Ecol. Indic.
2740 245 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 50 -0.6475000 0.0213000 Ecol. Indic.
2740 246 Mammides et al. 2017 2017 3.980 AR birds T diversity no_acoustic NO 50 -0.1003000 0.0213000 Ecol. Indic.
2740 247 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 50 -0.0802000 0.0213000 Ecol. Indic.
2740 248 Mammides et al. 2017 2017 3.980 BIO birds T diversity no_acoustic NO 50 0.2237000 0.0213000 Ecol. Indic.
2740 249 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 50 0.3884000 0.0213000 Ecol. Indic.
2740 250 Mammides et al. 2017 2017 3.980 H birds T diversity no_acoustic NO 50 0.3095000 0.0213000 Ecol. Indic.
2740 251 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 50 0.3769000 0.0213000 Ecol. Indic.
2740 252 Mammides et al. 2017 2017 3.980 NDSI birds T diversity no_acoustic NO 50 0.2769000 0.0213000 Ecol. Indic.
2740 253 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 50 0.3654000 0.0213000 Ecol. Indic.
2740 287 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.0701000 0.1429000 Ecol. Indic.
2740 288 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6042000 0.1429000 Ecol. Indic.
2740 289 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6625000 0.1429000 Ecol. Indic.
2740 290 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.1003000 0.1429000 Ecol. Indic.
2740 291 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.1923000 0.1429000 Ecol. Indic.
2740 292 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4236000 0.1429000 Ecol. Indic.
2740 293 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.3769000 0.1429000 Ecol. Indic.
2740 302 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.0300000 0.1429000 Ecol. Indic.
2740 303 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6475000 0.1429000 Ecol. Indic.
2740 304 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6777000 0.1429000 Ecol. Indic.
2740 305 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.1003000 0.1429000 Ecol. Indic.
2740 306 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.2237000 0.1429000 Ecol. Indic.
2740 307 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4722000 0.1429000 Ecol. Indic.
2740 308 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.4847000 0.1429000 Ecol. Indic.
2740 313 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.0701000 0.1429000 Ecol. Indic.
2740 314 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6184000 0.1429000 Ecol. Indic.
2740 315 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6777000 0.1429000 Ecol. Indic.
2740 316 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.0601000 0.1429000 Ecol. Indic.
2740 317 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.2237000 0.1429000 Ecol. Indic.
2740 318 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4118000 0.1429000 Ecol. Indic.
2740 319 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.3316000 0.1429000 Ecol. Indic.
2740 328 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.0300000 0.1429000 Ecol. Indic.
2740 329 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6184000 0.1429000 Ecol. Indic.
2740 330 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6777000 0.1429000 Ecol. Indic.
2740 331 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.0601000 0.1429000 Ecol. Indic.
2740 332 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.2342000 0.1429000 Ecol. Indic.
2740 333 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4236000 0.1429000 Ecol. Indic.
2740 334 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.3884000 0.1429000 Ecol. Indic.
2740 341 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.1104000 0.1429000 Ecol. Indic.
2740 342 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6042000 0.1429000 Ecol. Indic.
2740 343 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6475000 0.1429000 Ecol. Indic.
2740 344 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.0701000 0.1429000 Ecol. Indic.
2740 345 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.2342000 0.1429000 Ecol. Indic.
2740 346 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4236000 0.1429000 Ecol. Indic.
2740 347 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.4477000 0.1429000 Ecol. Indic.
2740 352 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.0902000 0.1429000 Ecol. Indic.
2740 353 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6328000 0.1429000 Ecol. Indic.
2740 354 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6777000 0.1429000 Ecol. Indic.
2740 355 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.1003000 0.1429000 Ecol. Indic.
2740 356 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.2027000 0.1429000 Ecol. Indic.
2740 357 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4599000 0.1429000 Ecol. Indic.
2740 358 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.4356000 0.1429000 Ecol. Indic.
2745 2 Sueur et al. 2008 2008 4.810 H several T richness acoustic NO 10 1.7211000 0.1429000 PLoS One
2977 97 Jorge et al. 2018 2018 4.490 ACI birds T richness acoustic YES 9 0.5731000 0.1667000 Ecol. Indic.
2977 99 Jorge et al. 2018 2018 4.490 ADI birds T richness acoustic YES 9 -0.2672000 0.1667000 Ecol. Indic.
2977 100 Jorge et al. 2018 2018 4.490 AEI birds T richness acoustic YES 9 0.3123000 0.1667000 Ecol. Indic.
2977 101 Jorge et al. 2018 2018 4.490 BIO birds T richness acoustic YES 9 0.4181000 0.1667000 Ecol. Indic.
2977 102 Jorge et al. 2018 2018 4.490 H birds T richness acoustic YES 9 -0.1475000 0.1667000 Ecol. Indic.
2977 103 Jorge et al. 2018 2018 4.490 NDSI birds T richness acoustic YES 9 0.3352000 0.1667000 Ecol. Indic.
2977 205 Jorge et al. 2018 2018 4.490 ACI birds T richness acoustic YES 9 0.3123000 0.1667000 Ecol. Indic.
2977 207 Jorge et al. 2018 2018 4.490 ADI birds T richness acoustic YES 9 -0.4676000 0.1667000 Ecol. Indic.
2977 208 Jorge et al. 2018 2018 4.490 AEI birds T richness acoustic YES 9 0.5192000 0.1667000 Ecol. Indic.
2977 209 Jorge et al. 2018 2018 4.490 BIO birds T richness acoustic YES 9 0.3009000 0.1667000 Ecol. Indic.
2977 210 Jorge et al. 2018 2018 4.490 H birds T richness acoustic YES 9 -0.1582000 0.1667000 Ecol. Indic.
2977 211 Jorge et al. 2018 2018 4.490 NDSI birds T richness acoustic YES 9 0.3352000 0.1667000 Ecol. Indic.
2986 68 Raynor et al. 2017 2017 2.720 ACI birds T richness acoustic YES 6 0.2448000 0.3333000 Condor


Table S2 - Variable descriptions for Table S1.

vd <- data.frame(Variables = c("id", "entry", "journal", "year", "impact_factor", "index",
                               "taxa", "environ", "bio", "diversity_source", "pseudoreplication",
                               "n", "z", "var"),
                 Descriptions = c(
                      "Identification number for the study",
                      "Identification number for the effect size (row entry)",
                      "Journal where the study was published",
                      "Year of publication",  
                      "Impact factor of the Journal",
                      "Acoustic index used",
                      "Primary studied group (invertebrates, fish, anurans, birds, mammals or several)",
                      "Ecosystem type where recordings were collected (A for Aquatic, T for terrestrial)",
                      "Diversity metric used to correlate with acoustic index values",  
                      paste("Source of biological data. ",
                            "Sound recordings (coded as acoustic) or other source, ",
                            "i.e. literature, field surveys, etc. (coded as no_acoustic)."),
                      paste("Pseudoreplicated design to compute acoustic indices",
                            "relation with biodiversity (coded as YES). Coded as NO otherwise."),  
                      "Sample size adjusted for what was considered the number of true replicates",
                      "Fisher's Z effect size",
                      "Fisher's Z variance"
             ) 
           )
pander(vd, justify = "left")
Variables Descriptions
id Identification number for the study
entry Identification number for the effect size (row entry)
journal Journal where the study was published
year Year of publication
impact_factor Impact factor of the Journal
index Acoustic index used
taxa Primary studied group (invertebrates, fish, anurans, birds, mammals or several)
environ Ecosystem type where recordings were collected (A for Aquatic, T for terrestrial)
bio Diversity metric used to correlate with acoustic index values
diversity_source Source of biological data. Sound recordings (coded as acoustic) or other source, i.e. literature, field surveys, etc. (coded as no_acoustic).
pseudoreplication Pseudoreplicated design to compute acoustic indices relation with biodiversity (coded as YES). Coded as NO otherwise.
n Sample size adjusted for what was considered the number of true replicates
z Fisher’s Z effect size
var Fisher’s Z variance

Dataset overview

In what follows, we give a brief overview of the dataset mostly by way of tables and figures.

Our dataset comprised a total of 34 studies and 364 effect sizes. Therefore, most studies contributed with more than one effect size for the meta-analysis.

Table S3 - Number of effect sizes collected from each of the 34 studies included in the meta-analysis. ID corresponds to the study identification number in our dataset.

studies_n <- as.data.frame(table(df_tidy$id), stringsAsFactors = FALSE)
studies_n <- cbind(unique(df_tidy$authors), studies_n)
colnames(studies_n) <- c("Study", "ID", "Effect_sizes")
studies_n <- studies_n %>%
              select(ID, Study, Effect_sizes) %>%
              arrange(-Effect_sizes)

kable(studies_n, format = "html") %>%
  kable_styling(c("striped"))
ID Study Effect_sizes
2740 Mammides et al. 2017 84
53 Moreno-Gomez 2019 42
87 Eldridge et al. 2018 28
80 Staaterman et al. 2017 24
90 Ferreira et al. 2018 24
96 Izaguirre et al.  2018 24
10 Buscaino et al. 2016 22
2 Desjonquères et al. 2015 12
11 Bertucci et al. 2016 12
89 Gage et al. 2017 12
2977 Jorge et al. 2018 12
70 Bolgan et al. 2018 11
9 Harris et al. 2016 6
86 Indraswari et al. 2018 6
427 Fuller et al. 2015 6
14 Wa Maina et al. 2016 4
60 Patrick Lyon et al. 2019 4
77 Fairbrass et al. 2017 4
92 Torti et al. 2018 4
15 Roca & Proulx 2016 3
45 McLaren 2012 3
13 McWilliam & Hawkin 2013 2
41 Paisley-Jones 2011 2
44 Machado et al. 2017 2
251 Buxton et al. 2016 2
4 Parks et al. 2014 1
6 Boelman et al. 2007 1
17 Zhang et al. 2015 1
37 Picciulin et al. 2016 1
1132 Depraetere et al. 2012 1
1177 Joo et al. 2011 1
1262 Pieretti et al. 2011 1
2745 Sueur et al. 2008 1
2986 Raynor et al. 2017 1


The most studied acoustic index was ACI and the most used biodiversity metric was ‘species richness’.

Table S4 - Number of effect sizes and studies per moderator levels.

# Table for moderator levels
mods <- c("index", "bio", "diversity_source", "environ")
sample_sizes <- do.call(rbind, lapply(mods, function(x) n_studies_entries(df_tidy, x)))
# Format output
sample_sizes <- cbind(row.names(sample_sizes), sample_sizes)
sample_sizes <- as.data.frame(sample_sizes)
names(sample_sizes) <- c("Moderator_levels", "Effect_sizes", "Studies")
sample_sizes$Moderator_levels <- str_replace(sample_sizes$Moderator_levels, "^([a-z])", toupper)
n_row <- nrow(sample_sizes)
sample_sizes$Moderator_levels[(n_row - 1):n_row] <- c("Aquatic", "Terrestrial")

kable(sample_sizes, format = "html", row.names = FALSE) %>%
    kable_styling("striped", full_width = FALSE, position = "left") %>%
    row_spec(0, font_size = 16, bold = TRUE) %>%
    pack_rows("Acoustic indices", 1, 11) %>%
    pack_rows("Biodiversity metrics", 12, 16) %>%
    pack_rows("Diversity source", 17, 18) %>%
    pack_rows("Environment", 19, 20)
Moderator_levels Effect_sizes Studies
Acoustic indices
ACI 113 25
ADI 38 12
AEI 34 8
AR 18 5
BIO 36 10
H 55 16
Hf 12 3
Ht 15 4
M 5 2
NDSI 33 10
NP 5 2
Biodiversity metrics
Abundance 27 6
Diversity 49 9
Richness 187 21
Sound_abundance 66 11
Sound_richness 35 3
Diversity source
Acoustic 200 26
No_acoustic 164 11
Environment
Aquatic 95 10
Terrestrial 269 24

Visual description of eligible studies

We gathered studies from 6 of the 7 continents (no studies in Antarctica). Most studies were conducted in USA, France, Australia and Brazil.

knitr::include_graphics("rmd/mapa.png")

Figure S1 - The geographic distribution of the study sites corresponding to the 35 studies used in meta-analysis. The coloring of countries exhibits a white to black gradient relative to an increase in the number of studies contributed by each country. The colored dots discriminate between different groups of studied taxon.


The performance of acoustic indices as a biodiversity indicators was mainly assessed with species richness and sound abundance as biodiversity metrics.

knitr::include_graphics("rmd/Fig.S3a.jpg", )

Figure S2 - Number of studies per acoustic index classified by the diversity measure used to assess the correlation between the acoustic index and biodiversity.


Most studies collected in our dataset assessed the relation between acoustic indices and biodiversity using recordings of bird sounds.

knitr::include_graphics("rmd/Fig.S3b.jpg")

Figure S3 - Number of studies per acoustic index classified by their studied taxon.

Meta-analysis

We clustered effect sizes within their corresponding studies and conducted multilevel meta-analysis using Fisher’s Z as our response variable. The multilevel structure accounted for the correlation structures within studies and thus allowed the inclusion of multiple effect sizes per study.

Summary effect size

We tested whether acoustic indices were good estimators of biodiversity by computing an intercept-only model. The resulting summary effect size not only gives a clue of whether acoustic indices are performing well in estimating biodiversity across the literature, but also allows us to check if there is substantial heterogeneity in our effect sizes that could be explained by moderators.

Intercept-only meta-analysis output 
res_main <- rma.mv(z, var, random = ~1 | id/entry, data = df_tidy)
res_main
## 
## Multivariate Meta-Analysis Model (k = 364; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0458  0.2139     34     no        id 
## sigma^2.2  0.1755  0.4190    364     no  id/entry 
## 
## Test for Heterogeneity:
## Q(df = 363) = 2220.9097, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.3461  0.0577  6.0014  <.0001  0.2331  0.4591  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Table S5 - Resulting estimates from intercept-only model converted to Pearson’s correlation. “Estimate” is the Pearson’s r summary effect size. “CI.lb” and “CI.ub” are the confidence intervals lower and upper bounds, respectively.

r_main <- sapply(c(r = res_main$b, CI.lb = res_main$ci.lb, CI.ub = res_main$ci.ub), z2r)
r_main_pander <- r_main
names(r_main_pander) <- c("Estimate", names(r_main)[2:3])
pander(r_main_pander)
Estimate CI.lb CI.ub
0.3329 0.2289 0.4294

The summary estimate indicates that acoustic indices showed a moderate correlation with biodiversity metrics. However this result does not say nothing about differences in performance between acoustic indices or context-dependencies due to, for example, different environments or diversity metrics. For this we would need to inspect moderators, but before we need to check if our intercept-only model has unexplained variance that can be partitioned by our chosen moderators.

Effect sizes in ascending order

The amount of heterogeneity in effect sizes can be coarsely inspected by plotting all effect sizes and respective variances, and see their dispersion along the x-axis.

df_all_plt <- mutate(df_tidy, z = z2r(z), var = z2r(var))
r_main <- as.data.frame(t(r_main))
nudge <- 2
overall_y <- -nudge - 1
ggplot(data = df_all_plt, aes(x = z, y = reorder(entry, -z))) + 
    geom_errorbarh(aes(xmin = z - 1.96 * var, xmax = z + 1.96 * var),
                   height = 0, size = 0.5, color = "grey", 
                   position = position_nudge(y = nudge)) + 
    geom_point(size = 0.8, color = "darkgreen", 
               position = position_nudge(y = nudge)) +
    geom_segment(aes(x = z2r(res_main$b), y = overall_y, 
                     xend = z2r(res_main$b), 
                     yend = nrow(df_all_plt) + nudge + 10), 
                     color = alpha("forestgreen", 0.7), linetype = 2, size = 0.5) +   
    geom_vline(xintercept = 0, linetype = 1) +
    # Insert overall estimate
    geom_errorbarh(aes(xmin = r_main$CI.lb, xmax = r_main$CI.ub, y = overall_y), 
                   color = "grey") +
    geom_point(data = r_main, aes(x = r, y = overall_y), size = 3, color = "forestgreen") +
    geom_hline(yintercept = nudge - 1, color = alpha("black", 0.5), linetype = 5, size = 1) + 
    annotate("text", x = -1.4, y = overall_y, label= "Overall estimate", size = 4, adj = "right") + 
    scale_y_discrete(expand = c(0.025, 0.01)) +
    xlab("Effect size (r)") +
    ylab("Dataset entries ordered by effect size magnitude") +
    theme_minimal() + 
    theme(axis.text.x = element_text(size = 12, color = "black"),
          axis.text.y = element_blank(),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title = element_text(size = 14),
          panel.grid.major.y = element_blank(), 
          legend.position = "none"
    )

Figure S4 - Pearson correlation effect sizes (r) in ascending order of magnitude from all dataset entries. Effect sizes higher than 0 (vertical line) represent a positive correlation between acoustic indices and diversity. Effect sizes below 0 indicate a negative correlation between acoustic indices and diversity. The green circles above the dashed horizontal line, are effect sizes means and corresponding 95% confidence intervals (grey horizontal lines). Below the dashed line is the summary effect size (green circle) and corresponding 95% confidence interval (grey horizontal lines) obtained after running the intercept-only model.

Check heterogeneity

We quantified heterogeneity with the I² statistic, which estimates the proportion of unknown variation in effect sizes not attributed to sampling error variance.


Table S6 - Unnacounted heterogeneity of the intercept-only model as measured by I2 statistic. Within study heterogeneity (level 2) corresponds to the unnacounted variation that is found on effect sizes within studies, and between study heterogeneity corresponds to the unnacounted variation between studies (level 3).

font_css <- "font-family: Arial"
Is <- multilevel_I(res_main) * 100
Is_df <- data.frame(Is[1], Is[2])
names(Is_df) <- c("Within study", "Between study")
rownames(Is_df) <- c("% Unexplained variation")
total_I <- paste("Total heterogeneity: ", round(Is[1] + Is[2], 2), "%")
header <- c(3)
names(header) <- c(total_I)
kable(Is_df, format = "html", digits = 2) %>%
  kable_styling(c("striped", "bordered"), full_width = FALSE, 
                position = "center") %>%
  add_header_above(header, font_size = 16, bold = TRUE,
                   extra_css = font_css) %>%
  row_spec(0, font_size = 14, extra_css = font_css) %>%
  row_spec(1, font_size = 12, align = "center")
Total heterogeneity: 85.13 %
Within study Between study
% Unexplained variation 17.61 67.52 
mlm.variance.distribution(res_main)

Figure S5 - Visual representation of how variance was distributed over the multilevel structure of the intercept-only model. Within study heterogeneity (level 2) corresponds to the unnacounted variation that is found on effect sizes within studies, and between study heterogeneity corresponds to the unnacounted variation between studies (level 3).


The value of I2 = 85% corresponding to the amount of heterogeneity that remains unnacounted for in the intercept-only model, gives a green signal to proceed with the use of moderators that can potentially explain some of this variation.

Analysis of moderators

We extended the previous intercept-only model with the inclusion of moderators as fixed factors. For these analysis, all moderator levels with less than 5 studies were excluded as low study sizes are more liable to produce biased estimates. This led to the removal of the acoustic indices ‘Hf’, ‘Ht’, ‘M’ and ‘NP’, and the biodiversity parameter ‘sound richness’ from model fitting procedures.

Sub-group analysis

We conducted sub-group analysis with acoustic index as a moderator to assess which acoustic indices best correlate with biodiversity.

To specifically test whether the effect size estimates from each acoustic index were different from zero we removed the model intercept.

df_indices <-  clear_moderators(df_tidy, "index")
## Levels dropped from dataframe:
##  Moderator index
##       Hf Ht M NP
Meta-analysis output with acoustic indices as moderator and no intercept. 
res_indices <- rma.mv(z, var, random = ~ 1 | id/entry, mods = ~ index - 1, data = df_indices)
res_indices
## 
## Multivariate Meta-Analysis Model (k = 327; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0295  0.1716     34     no        id 
## sigma^2.2  0.1710  0.4135    327     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 320) = 1876.0325, p-val < .0001
## 
## Test of Moderators (coefficients 1:7):
## QM(df = 7) = 70.5454, p-val < .0001
## 
## Model Results:
## 
##            estimate      se    zval    pval    ci.lb   ci.ub 
## indexACI     0.3809  0.0685  5.5596  <.0001   0.2466  0.5152  *** 
## indexADI     0.2493  0.0977  2.5506  0.0108   0.0577  0.4408    * 
## indexAEI     0.0396  0.1048  0.3774  0.7059  -0.1658  0.2449      
## indexAR      0.0780  0.1354  0.5756  0.5649  -0.1875  0.3434      
## indexBIO     0.1950  0.1012  1.9266  0.0540  -0.0034  0.3934    . 
## indexH       0.5511  0.0903  6.1036  <.0001   0.3742  0.7281  *** 
## indexNDSI    0.4557  0.1037  4.3944  <.0001   0.2524  0.6589  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Table S7 - Resulting estimates from sub-group analysis. The ‘Estimate’ column is the Pearson correlation effect size; SE is the standard error and CI.lb and CI.up, the lower and upper bounds of the confidence intervals, respectively.

df_pred_indices <- get_predictions(res_indices, intercept = FALSE)
df_pred_indices$coef <- str_remove(df_pred_indices$coef, "index")
names(df_pred_indices) <- c("Index", "Estimate", "SE", "CI.lb", "CI.ub")
pander(df_pred_indices)
Index Estimate SE CI.lb CI.ub
ACI 0.363 0.068 0.242 0.474
ADI 0.244 0.097 0.058 0.414
AEI 0.04 0.104 -0.164 0.24
AR 0.078 0.135 -0.185 0.331
BIO 0.193 0.101 -0.003 0.374
H 0.501 0.09 0.358 0.622
NDSI 0.427 0.103 0.247 0.578 
nentries_index <- rowSums(table(df_indices$index, df_indices$id))
nstudies_index <- rowSums(ifelse(table(df_indices$index, df_indices$id) > 0, 1, 0))
n_index <- paste0(nentries_index, " (", nstudies_index, ")")

df_indices_plt <- data.frame("index" = names(nstudies_index),
                             "es" = z2r(res_indices$beta), 
                             "se" = z2r(res_indices$se), 
                             "ci.lb" = z2r(res_indices$ci.lb),
                             "ci.ub" = z2r(res_indices$ci.ub),
                             "n" = nstudies_index)

ggplot(data = df_indices_plt, aes(x = es, y = index)) + 
    geom_point(aes(color = index), size = 4) +
    geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub, color= index),
                   height = 0) + 
    geom_vline(xintercept = 0, linetype = 1) +
    geom_vline(xintercept = z2r(res_main$b), color = "forestgreen", 
               linetype = 2) + 
    scale_y_discrete(limits = rev(df_indices_plt$index)) +
    scale_color_brewer(palette="Dark2") +
    theme_minimal() + 
    xlab("Effect size (r)") +
    theme(axis.text.x = element_text(size = 12, color = "black"),
          axis.text.y = element_text(size = 13, color = "black"),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title.x = element_text(size = 14),
          axis.title.y = element_blank(),
          legend.position = "none"
    )

Figure S6 - Effect size mean estimates (circles) and corresponding 95% confidence intervals (horizontal lines) obtained from sub-group meta-analysis with acoustic indices as the grouping factor. Estimated effect sizes whose 95% confidence intervals do not overlap zero (black vertical line) indicate a positive correlation between acoustic indices and diversity if they are to the right of zero, or a negative correlation if they are to the left of zero. The dashed green vertical line represents the summary effect size obtained from the intercept only meta-analysis.


Meta-regression

We used meta-regression to check the effect of multiple moderators on the ability of acoustic indices to estimate biodiversity. We focused on four moderators that could alter the performance of biodiversity estimation, namely:

  1. acoustic index - which acoustic index was used to estimate biodiversity;
  2. biodiversity metrics – which metric of diversity was used to check the performance of acoustic index;
  3. environment – if recordings were done in aquatic or terrestrial environments;
  4. diversity source – if metric of diversity was obtained from examination of sound recordings of any other source (e.g. field sampling).

We set as intercept the following combination of moderator levels: ACI index, species richness, terrestrial environment and non-acoustic data source.

Due to low study sample size between most factor level combinations, we were constrained to use only an additive effects model.

mods <- c("index", "bio", "environ", "diversity_source")
df_full <- clear_moderators(df_tidy, mods)
## Levels dropped from dataframe:
##  Moderator index
##       Hf Ht M NP
##  Moderator bio
##       sound_richness
##  Moderator environ
##       
##  Moderator diversity_source
##
Meta-analysis with acoustic indices, biodiversity metrics, environment and diversity source as moderators. 
res_full <- rma.mv(z, var, random = ~1 | id/entry, 
                   mods = ~ index + bio + environ + diversity_source, 
                   data = df_full)
 
res_full
## 
## Multivariate Meta-Analysis Model (k = 296; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0355  0.1884     33     no        id 
## sigma^2.2  0.1738  0.4168    296     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 284) = 1730.2577, p-val < .0001
## 
## Test of Moderators (coefficients 2:12):
## QM(df = 11) = 27.4277, p-val = 0.0040
## 
## Model Results:
## 
##                           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                     0.3590  0.1416   2.5359  0.0112   0.0815   0.6365 
## indexADI                   -0.1294  0.1171  -1.1049  0.2692  -0.3590   0.1001 
## indexAEI                   -0.2916  0.1231  -2.3679  0.0179  -0.5329  -0.0502 
## indexAR                    -0.2735  0.1482  -1.8461  0.0649  -0.5639   0.0169 
## indexBIO                   -0.1449  0.1203  -1.2038  0.2287  -0.3807   0.0910 
## indexH                      0.1977  0.1092   1.8111  0.0701  -0.0162   0.4117 
## indexNDSI                   0.0840  0.1260   0.6667  0.5050  -0.1630   0.3310 
## bioabundance               -0.0815  0.1589  -0.5133  0.6078  -0.3929   0.2298 
## biodiversity               -0.0420  0.0950  -0.4423  0.6583  -0.2283   0.1442 
## biosound_abundance          0.2600  0.1470   1.7690  0.0769  -0.0281   0.5480 
## environA                   -0.0656  0.1484  -0.4422  0.6583  -0.3565   0.2252 
## diversity_sourceacoustic   -0.0091  0.1464  -0.0623  0.9504  -0.2962   0.2779 
##  
## intrcpt                   * 
## indexADI 
## indexAEI                  * 
## indexAR                   . 
## indexBIO 
## indexH                    . 
## indexNDSI 
## bioabundance 
## biodiversity 
## biosound_abundance        . 
## environA 
## diversity_sourceacoustic 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Multicollinearity

Multicollinearity among our moderators was inspected with VIF and found not to be an issue in our model (VIF < 1.7 for all moderators – threshold value of 3 (Zuur, Ieno, & Elphick, 2010).

Table S8 - VIF values obtained for each moderator level.

vif_meta <- as.data.frame(vif.rma(res_full))
vif_meta <- tibble::rownames_to_column(vif_meta, "Moderators")
colnames(vif_meta)[2] <- "VIF" 
pander(vif_meta)
Moderators VIF
indexADI 1.497
indexAEI 1.45
indexAR 1.25
indexBIO 1.478
indexH 1.472
indexNDSI 1.438
bioabundance 1.244
biodiversity 1.156
biosound_abundance 1.454
environA 1.393
diversity_sourceacoustic 1.623


Heterogeneity full model

Substantial heterogeneity remained to be explained after fitting the full model. So other factors not tested or an interaction among our moderators could have the ability to extract even more information from the dataset. For the latter, an increase in available studies on the correlation between acoustic indices and biodiversity is of the utmost important.

# Fit overall model with df_full (without levels with less than 5 studies)
res_main_full <- rma.mv(z, var, random = ~1 | id/entry, data = df_full)
sum_I_main_full <- sum(multilevel_I(res_main_full))
sum_I_full <- sum(multilevel_I(res_full))

cat("Heterogenity after fitting the full model\n\t", sum_I_full, "\n\n",
    "Difference heterogeneity between intercept only model and full model\n\t",
    sum_I_main_full - sum_I_full)
## Heterogenity after fitting the full model
##   0.8616913 
## 
##  Difference heterogeneity between intercept only model and full model
##   0.01210371


Full model results

Table S9 - Meta-regression results. The column “Coefficients” lists the model intercept and the levels of each moderator. The column “Estimate” is the estimated Pearson (r) correlation. “SE” is the standard error of the estimate. “CI” are the [lower] [upper] bounds of the confidence intervals.

df_pred_tbl <- get_predictions(res_full, format_table = TRUE, clean_labels = TRUE)
pander(df_pred_tbl)
Moderators Coefficients Estimate SE CI
Intercept Intercept 0.344 0.141 [0.081] [0.563]
Index ADI 0.226 0.147 [-0.061] [0.478]
Index AEI 0.067 0.152 [-0.228] [0.351]
Index AR 0.085 0.17 [-0.246] [0.399]
Index BIO 0.211 0.149 [-0.08] [0.469]
Index H 0.506 0.14 [0.273] [0.682]
Index NDSI 0.416 0.148 [0.15] [0.626]
Bio Abundance 0.271 0.164 [-0.047] [0.539]
Bio Diversity 0.307 0.144 [0.033] [0.537]
Bio Sound abundance 0.55 0.211 [0.197] [0.777]
Environment Aquatic 0.285 0.142 [0.012] [0.519]
Source Acoustic 0.336 0.108 [0.136] [0.51] 
df_pred <- get_predictions(res_full)
colnames(df_pred) <- tolower(str_remove(colnames(df_pred), "\\s.*"))
df_pred$moderators <- df_pred_tbl$Moderators
df_pred$coef <- factor(df_pred_tbl$Coefficients, 
                       levels = rev(df_pred_tbl$Coefficients))

plt_colors <- c("#000000", brewer.pal(n = 4, name = "Dark2"))
ggplot(data = df_pred, aes(x = z2r(estimate), y = coef, color = moderators)) + 
    geom_point(size = 3) +
    geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub), height = 0, size = 1) +
    geom_vline(xintercept = 0, linetype = 1) + 
    scale_color_manual(values = plt_colors, name = "Moderators") + 
    theme_minimal() + 
    xlab("Effect size (r)") +
    theme(axis.text.x = element_text(size = 13, color = "black"),
          axis.text.y = element_text(size = 13, color = "black"),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title.y = element_blank(),
          axis.title.x = element_text(size = 14),
          panel.grid.major.y = element_blank(),
          legend.title = element_text(hjust = 0.5, size = 14),
          legend.text = element_text(size = 14)
    )

Figure S7 - Effect size mean estimates (circles) and corresponding 95% confidence intervals (horizontal lines) obtained from meta-regression analysis with moderators: acoustic indices (Index), biodiversity metrics (Bio), environment (Environment) and acoustic source (Source). Estimated effect sizes whose 95% confidence intervals do not overlap zero (black vertical line) indicate a positive correlation between acoustic indices and diversity if they are to the right of zero, or a negative correlation if they are to the left of zero.


Test of moderators

We checked if our choice of moderators explained some of the variation in our effects sizes by computing a Wald-type test on the null hypothesis that moderator levels’ estimates are jointly equal to zero (Viechtbauer et al., 2015).

Table S10 - Wald-type tests for all moderators (first row), and for each moderator separately (remaining rows). “Q” is the Wald statistic. “df” are the degrees of freedom. “p” is the probability that moderator estimates came from a chi-square distribution, where all estimates are equal to zero. So a p-value < 0.05 gives support against the null hypothesis that moderator levels estimates are equal to zero (i.e. they do not explain variation in effect sizes).

imp_mods <- matrix(nrow = length(mods) + 1, ncol = 4, 
                   dimnames = list(NULL, c("Moderator", "Q", "df", "p")))
imp_mods <- as.data.frame(imp_mods)
# Add importance of all moderators
imp_mods[1, 2:ncol(imp_mods)] <- res_full[c("QM", "m", "QMp")]
imp_mods$Moderator[1] <- "All moderators"

for(i in 2:nrow(imp_mods)){
  wald_test <- anova(res_full, 
                     btt=grep(mods[i - 1], rownames(res_full$b)))[c("QM", "m","QMp")]
  imp_mods[i, 2:ncol(imp_mods)] <- wald_test
}
imp_mods$Moderator[2:nrow(imp_mods)] <- c("Acoustic indices", 
                                          "Biodiv. parameters", 
                                          "Environment", "Diversity source")
imp_mods <- mutate_if(imp_mods, is.numeric, round, 3)

pander(imp_mods)
Moderator Q df p
All moderators 27.43 11 0.004
Acoustic indices 22.35 6 0.001
Biodiv. parameters 3.561 3 0.313
Environment 0.196 1 0.658
Diversity source 0.004 1 0.95

We found that the acoustic indices explained most of the variation in our full model. Hence, this suggests that acoustic indices are not equally performing when it comes to estimate biodiversity.


Difference between moderator levels

To assess pairwise-comparisons between moderator level estimates, we selected the moderator levels with the highest effect size estimates and compared these with effect size estimates for the other levels of the same moderator. For this, we again use a Wald-type test with one degree of freedom, on the null hypothesis that the difference between the two levels is equal to zero. Note that, if a moderator has only two levels, the comparison is directly retrieved from the model output.

Acoustic indices pairwise-comparisons

We compare the best indices, H and NDSI, with the other indices. We did not use ACI for comparisons, as comparisons can be obtained directly from the full model output.

H index

The pairwise-comparison for H index, suggest that the H index correlates better with biodiversity than the indices ADI, AEI, AR and BIO.


Table S11 - Wald-type tests for the constrasts between acoustic index H with all other acoustic indices. The column “Compared” expresses the comparison, in this cases it is the difference between the estimate H and the estimate of each of the other acoustic indices. The column “Estimate” is the estimate obtained from the difference expressed in the previous column. “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively. “QM” is the Wald statistic. “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the H index and the estimate of the other index.

# Test differences between H and other indices
H_comp <- compare_moderators(df_full, res_full, "index", "H")
kable(H_comp, format = "html", digits = 3) %>%
  kable_styling(c("strip", "condensed"))
Compared Estimate SE CI.lb CI.up QM p
H - ADI 0.327 0.122 0.089 0.566 7.223 0.007
H - AEI 0.489 0.127 0.241 0.738 14.901 0.000
H - AR 0.471 0.152 0.173 0.769 9.623 0.002
H - BIO 0.343 0.124 0.099 0.586 7.591 0.006
H - ACI 0.198 0.109 -0.016 0.412 3.280 0.070
H - NDSI 0.114 0.130 -0.141 0.369 0.765 0.382
NDSI index

The pairwise-comparison for NDSI index, suggest that the NDSI index correlates better with biodiversity than the indices AEI and AR.


Table S12 - Wald-type tests for the constrasts between acoustic index NDSI with all other acoustic indices. The column “Compared” expresses the comparison, in this cases it is the difference between the estimate NDSI and the estimate of each of the other acoustic indices. The column “Estimate” is the estimate obtained from the difference expressed in the previous column. “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively. “QM” is the Wald statistic. “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the NDSI index and the estimate of the other index.

# Test differences between NDSI and other indices
NDSI_comp <- compare_moderators(df_full, res_full, "index", "NDSI")
kable(NDSI_comp, format = "html", digits = 3) %>%
  kable_styling(c("strip", "condensed"))
Compared Estimate SE CI.lb CI.up QM p
NDSI - ADI 0.213 0.133 -0.047 0.474 2.586 0.108
NDSI - AEI 0.376 0.138 0.106 0.645 7.442 0.006
NDSI - AR 0.358 0.161 0.041 0.674 4.914 0.027
NDSI - BIO 0.229 0.135 -0.036 0.494 2.869 0.090
NDSI - H -0.114 0.130 -0.369 0.141 0.765 0.382
NDSI - ACI 0.084 0.126 -0.163 0.331 0.444 0.505
Biodiversity metrics pairwise-comparisons

Since sound abundance measures seemed to be related with a best performance of the acoustic indices ability to correlate with biodiversity, we use sound abundance to compute pairwise comparisons with the other biodiversity metrics.

The pairwise-comparison for sound abundance gave marginal support (at p < 0.05) for the null hypothesis of no difference between sound abundance and other biodiversity metrics.


Table S13 - Wald-type tests for the contrasts between the biodiversity metric sound abundance with all other biodiversity metrics. The column “Compared” expresses the comparison, in this cases it is the difference between the estimate sound abundance and the estimate of each of the other biodiversity metrics. The column “Estimate” is the estimate obtained from the difference expressed in the previous column. “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively. “QM” is the Wald statistic. “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the sound abundance metric and the estimate of the other metric.

# Test difference between sound_abundance and other bio levels
sound_abund_comp <- compare_moderators(df_full, res_full, "bio", "sound_abundance")
kable(sound_abund_comp, format = "html", digits = 2) %>%
  kable_styling(c("strip", "condensed"))
Compared Estimate SE CI.lb CI.up QM p
sound_abundance - abundance 0.34 0.21 -0.08 0.76 2.53 0.11
sound_abundance - diversity 0.30 0.17 -0.03 0.64 3.09 0.08
sound_abundance - richness 0.26 0.15 -0.03 0.55 3.13 0.08


Publication bias

We assessed publication bias both, visually with funnel plots and statistically with Egger’s regression.

A funnel plot usually shows the relationship between effect sizes and standard errors. In a symmetric funnel plot, the dispersion of effect sizes should get narrower as standard error decreases.

Due to the multilevel structure of our dataset, we used meta-analytic residuals instead of effect sizes to reduce the effect of independence assumptions. We should consider publication bias as an issue if residuals are outside the expected symmetry of the funnel shape, and if some of the funnel sections do not contain any residual.

To statistically test for funnel plot symmetry we used Egger’s regression with no intercept. A non-significant inverse variance weighted regression of the residuals over the standard error, indicates that the deviation of the residuals from the funnel plot shape is not greater than what would be expected by chance in a symmetric funnel plots.

# Testing model residuals
resid <- rstandard(res_full)
eggers <- regtest(x = resid$resid, sei =sqrt(df_full$var), model = "lm")
funnel(res_full, 
       back = "white", 
       xlab = "Model Residuals", 
       ylab = "Std. Error", 
       pch = 21,
       col = "darkblue",
       cex = 1.1,
       lwd = 2
)
# Put eggers regression results on funnel plot
eggers_round <- round(eggers$pval, 2)
plt_text <- paste0("Regression test for plot symmetry \n\t\t  p = ", eggers_round, "\n\n")
legend(legend = plt_text, x = 0.5, y = -0.01, bg = alpha("darkgrey", 0.2))

Figure S7 - Funnel plot (dashed triangle) with the relation between model residuals from the meta-regression model, and effect size standard error. Absence of publication bias is represented by a scattered and symmetric distribution of values (blue hollow dots) within the funnel. The box on the top right is the p-value from Egger’s regression, which means that we failed to reject the null hypothesis of funnel symmetry (p = 0.53).

Output of Egger’s regression. 
eggers$fit
## 
## Call:
## lm(formula = yi ~ X - 1, weights = 1/vi)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.9740  -1.0312  -0.2052   0.8569  17.3423 
## 
## Coefficients:
##      Estimate Std. Error t value Pr(>|t|)   
## X    -0.12346    0.04217  -2.927  0.00369 **
## Xsei  0.15381    0.23167   0.664  0.50727   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.466 on 294 degrees of freedom
## Multiple R-squared:  0.05028,    Adjusted R-squared:  0.04382 
## F-statistic: 7.782 on 2 and 294 DF,  p-value: 0.0005091

We could not find strong indications of publication bias. Notwithstanding the visual inspection of the funnel plot shows that there are some gaps in the dispersion of the dots in the funnel plot (special at the top, and at the bottom left corner).


Sensibility analysis

Pseudoreplication

Pseudoreplicated designs were widespread in our selected studies. Therefore, to determine the influence of effect size estimates from pseudoreplicated studies in our meta-analysis, we contrasted the effect size estimate for pseudoreplicated and non-pseudoreplicated studies. For this we conducted a meta-analysis with pseudoreplication as the single binary moderator and observed if the resulting estimate of the contrast between both designs overlapped zero.

Meta-analysis with pseudoreplication moderator 
no_out <- capture.output({
            df_pseudorep <-  clear_moderators(df_tidy, "pseudoreplication")  
          })
  
res_pseudorep <- rma.mv(z, var, random = ~1 | id/entry, mods = ~ pseudoreplication, 
                        data = df_pseudorep)
res_pseudorep
## 
## Multivariate Meta-Analysis Model (k = 364; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0508  0.2254     34     no        id 
## sigma^2.2  0.1754  0.4188    364     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 362) = 2176.1806, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4077, p-val = 0.5231
## 
## Model Results:
## 
##                       estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                 0.3759  0.0713   5.2759  <.0001   0.2363  0.5156  *** 
## pseudoreplicationYES   -0.0787  0.1232  -0.6385  0.5231  -0.3201  0.1628      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(data = df_pseudorep, aes(x = pseudoreplication, y = z2r(z))) +
  geom_boxplot(aes(color = pseudoreplication)) + 
  geom_jitter(aes(color = pseudoreplication, size = n), width = 0.2, alpha = 0.5) +
  geom_hline(yintercept = z2r(res_main$b), color = "chartreuse4", linetype = 2) + 
  ylab("Effect Size (r)") + 
  xlab("Pseudoreplicated") + 
  scale_color_brewer(palette = "Set1") + 
  scale_x_discrete(labels = c("No", "Yes")) +
  coord_flip() +
  theme_minimal() +
  theme(legend.position = "none",
        axis.text.y = element_text(size = 12, color = "black"),
        axis.text.x = element_text(size = 11, color = "black"),
        axis.title.y = element_text(size = 14),
        axis.title.x = element_text(size = 12),
        axis.line.x = element_line(color = "black")
  )

Figure S8 - Boxplot comparing effect size mean values of sampling designs considered pseudoreplicated (“Yes” on the vertical axis) against sampling designs not considered pseudoreplicated (“No” on the vertical axis). The circles represent each individual effect size mean value, and the circle size indicates the relative sample size of the effect size. The dashed green vertical line shows the position of summary effect size obtained from the intercept only meta-analysis.


We failed to find differences between the estimates of pseudoreplicated and non-pseudoreplicated designs. Thus, we believe that our adjustment of sample sizes, was a sufficient treatment for pseudoreplication in our meta-analysis.

Outliers

We visually inspected the presence of outlier studies using Cook’s distance clustered by studies. The Cook’s distance for a given study, refers to how far, on average, effect size estimates will move if the study in question is dropped from model fitting (Viechtbauer & Cheung, 2010). We considered a study an outlier if its Cook’s distance was higher than the average of all computed Cook’s distances.

Cook’s distance

### Cook's distances for each study!

cooks_dist <- cooks.distance(res_full, cluster=df_full$id)
df_full$id <- as.character(df_full$id)

df_cooks <- data.frame(id = names(cooks_dist), cooks_dist = cooks_dist)

df_cooks <- df_cooks %>% 
              left_join(df_full, by = "id") %>%
              select(id, authors, cooks_dist)
# Remove leading spaces
df_cooks$authors <- str_remove(df_cooks$authors, "^\\s")
df_cooks <- df_cooks %>%
              filter(!duplicated(authors)) %>%
              arrange(authors)

ggplot(data = df_cooks, aes(x = authors, y = cooks_dist, group = 1)) + 
  geom_point(color = "deepskyblue4") +
  geom_line(color = "deepskyblue4") + 
  geom_segment(aes(xend=authors), yend = 0, color = "darkgrey", linetype = 2) + 
  geom_hline(yintercept = mean(cooks_dist), color = "darkred", linetype = 2) + 
  xlab("Studies") + ylab("Cook's Distance") + 
  scale_x_discrete(limits = rev(df_cooks$authors)) + 
  theme_minimal() + 
  theme(
        axis.text = element_text(color = "black"),
        axis.line = element_line(color = "black"),
  ) + 
  coord_flip()

Figure S9 - Cook’s distance values for each study (blue dots on the figure) and average Cook’s distance over all studies indicated as a dashed vertical red line. The Cook’s distance for a given study can be interpreted as the distance between the entire set of predicted values once with this study included and once with the this study excluded from the model fitting procedure. On the y-axis are the studies identified by first author and year. The x-axis corresponds to the Cook’s distance values.

Check outlier studies

Here, we examine outlier studies to discriminate possible reasons for their influence.

outliers <- df_cooks[which(df_cooks$cooks_dist > mean(df_cooks$cooks_dist)), ]$id

df_outliers <- df_full[which(df_full$id %in% outliers), ]

ggplot(data = df_outliers, aes(x = z2r(z), y = id)) + 
  geom_boxplot(aes(color = id), fill = NA, width = 0.3) + 
  geom_jitter(height = 0.1, aes(color = id)) + 
  scale_color_brewer(palette = "Set2") + 
  scale_y_discrete(labels = rev(unique(df_outliers$authors))) + 
  xlab("Effect size (r)") +
  geom_vline(xintercept = z2r(res_main$b), color = "chartreuse4", linetype = 2) + 
  theme_minimal() + 
  theme(
    axis.title.y = element_blank(),
    axis.text.y = element_text(size = 11, color = "black"),
    axis.text.x = element_text(size = 11, color = "black"),
    axis.line.x = element_line(color = "black"),
    legend.position = "none"
  )

Figure S10 - Boxplot and distribution of of effect size values (dots) of the two studies identified as outliers. The y-axis identifies the study, and the x-axis corresponds to the Pearson r effect size. The green vertical dashed line is the summary effect obtained in the intercept-only model.


Both outlier studies used birds as the organism of study, and assessed multiple acoustic indices (Gage et al. (2017) examined ACI, ADI, AEI, BIO, H, NDSI indices; and Mammides et al. (2017) examined ACI, ADI, AEI, AR, BIO, H, NDSI indices). The main difference was that Gage et al. (2017) used acoustic recordings to get biodiversity measures while Mammides et al. (2017) relied on non acoustic sources of biodiversity information.

The box plots and effect size dispersion, suggest that the study by Gage et al. (2017) contributed overdispersed effect sizes values, including some at the lower and high ends of the Pearson effect size scale of (-1 to 1).

The Mammides et al. (2017) study contributed a total 84 effect sizes, also dispersed over a wide range. The number of effect sizes per se (23% of total effect sizes) could be responsible for its high value of Cook’s distance.

Removing outlier studies and examining results

We evaluated the robustness of our results by removing the outliers from the dataset, and running the meta-regression model without the outlier studies.

Meta-analysis with outliers removed 
df_no_outliers <- df_full[-which(df_full$id %in% outliers), ]

res_no_outliers <- rma.mv(z, var, random = ~1 | id/entry, 
                   mods = ~ index + bio + environ + diversity_source, 
                   data = df_no_outliers)

res_no_outliers
## 
## Multivariate Meta-Analysis Model (k = 200; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.1225  0.3500     31     no        id 
## sigma^2.2  0.0557  0.2360    200     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 188) = 472.4368, p-val < .0001
## 
## Test of Moderators (coefficients 2:12):
## QM(df = 11) = 6.3074, p-val = 0.8521
## 
## Model Results:
## 
##                           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                     0.6457  0.1870   3.4522  0.0006   0.2791  1.0123 
## indexADI                   -0.2059  0.1118  -1.8412  0.0656  -0.4251  0.0133 
## indexAEI                   -0.0753  0.1260  -0.5977  0.5500  -0.3224  0.1717 
## indexAR                    -0.0770  0.2082  -0.3700  0.7114  -0.4850  0.3310 
## indexBIO                   -0.0992  0.1192  -0.8319  0.4054  -0.3328  0.1345 
## indexH                     -0.0357  0.1058  -0.3372  0.7360  -0.2430  0.1716 
## indexNDSI                  -0.0164  0.1406  -0.1165  0.9073  -0.2919  0.2591 
## bioabundance               -0.1566  0.1512  -1.0355  0.3005  -0.4530  0.1398 
## biodiversity               -0.1064  0.1298  -0.8199  0.4122  -0.3607  0.1479 
## biosound_abundance          0.1597  0.2105   0.7587  0.4480  -0.2528  0.5722 
## environA                   -0.1907  0.2057  -0.9271  0.3539  -0.5939  0.2125 
## diversity_sourceacoustic   -0.1379  0.1909  -0.7226  0.4700  -0.5120  0.2362 
##  
## intrcpt                   *** 
## indexADI                    . 
## indexAEI 
## indexAR 
## indexBIO 
## indexH 
## indexNDSI 
## bioabundance 
## biodiversity 
## biosound_abundance 
## environA 
## diversity_sourceacoustic 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# With outliers results from full model results 
df_pred$df <- "with_outliers"
# No outliers resuls in dataframe
df_no_outliers_plt <- get_predictions(res_no_outliers)
df_no_outliers_plt$moderators <- df_pred$moderators
df_no_outliers_plt$coef <- factor(df_pred$coef, 
                                  levels = rev(df_pred$coef))
df_no_outliers_plt$df <- "no_outliers"

df_examine_outliers <- rbind(df_pred, df_no_outliers_plt)

plt_colors <- c("skyblue4", "goldenrod3")#"seagreen")
pd <- position_dodge(0.6)
n_rows <- nrow(res_full$b)

ggplot(data = df_examine_outliers, aes(x = estimate, y = coef, color = df, position = df)) + 
    geom_point(position = pd, size = 2.3) +
    geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub), height = 0, position = pd, size = 0.8) + 
    geom_vline(xintercept = 0, linetype = 1, color = "black") + 
    #scale_y_discrete(labels =  rev(y_labels)) +
    scale_color_manual(values = plt_colors, name = "Dataset", 
                       labels = c("No outliers", "Full dataset")) + 
    geom_hline(yintercept= seq(1, n_rows - 1) + 0.5, linetype = 3, color = "black") + 
    theme_minimal() + 
    xlab("Effect size (r)") +
    theme(axis.text.x = element_text(size = 12, color = "black"),
          axis.text.y = element_text(size = 13, color = "black"),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title.y = element_blank(),
          axis.title.x = element_text(size = 14),
          panel.grid.major.y = element_blank(),
          legend.title = element_text(hjust = 0.5, size = 12),
          legend.text = element_text(size = 11)
    )

Figure S11 - Contrast of model estimates obtained with meta-regression analysis over the full dataset (yellow) and over the dataset with outliers removed (blue). Mean estimates are represented with circles and corresponding 95% confidence intervals with horizontal lines. We considered outliers every study that had a Cook’s distance value higher than the mean of all Cook distances. Model moderators were acoustic indices (ADI, AEI, AR, BIO, H, NDSI, with ACI as intercept), diversity metric (Abundance, Diversity, Sound abundance, with Richness as intercept), environment (Aquatic, with Terrestrial as intercept), diversity source (Acoustic, with Non-Acoustic as intercept). The solid vertical black line represents a null effect size.


The results are similar with major overlap between confidence intervals, specially for the most conclusive results in the full dataset. It seems that removing outliers had a tendency to generate stronger mean effect size estimates of the correlation between acoustic indices and biodiversity.

Heterogeneity for the intercept-only model with no outliers 
res_main_no_outliers <- rma.mv(z, var, random = ~1 | id/entry, data = df_no_outliers)

Is_no_outliers <- multilevel_I(res_main_no_outliers)

sum(Is_no_outliers)
## [1] 0.8256989

Effect size tendencies

We visually inspected tendencies in the effect size over year of publication, and impact factor of the journal.

Publication year

This figure indicates a decrease in effect size values over the years. ?SOME DISCUSSION HERE FOR REASONS? Roughly after the year of 2015, we see a surge in published studies so in the next figure we make a close up of published studies after 2015.

plt_color <- "darkorchid4"
ggplot(df_tidy, aes(x = year, y = z2r(z))) + 
    geom_jitter(aes(size = n), shape = 21, 
                fill = alpha(plt_color, 0.5), 
                color = plt_color) + 
    geom_hline(yintercept=0, linetype = 2) +
    geom_smooth(method='lm', color = plt_color) +
    labs(y = "Effect size (r)", x = "Publication Year") +  
    scale_x_continuous(breaks = seq(min(df_tidy$year), 
                                    max(df_tidy$year), by = 2)) +
    theme_minimal() +
    theme(
        axis.line.y = element_line(color = "black"),
        axis.text = element_text(color = "black", size = 12),
        axis.title = element_text(color = "black", size = 14),
        legend.position = "none"
    )

Figure S12 - Relation between effect size mean values (circles) and publication year. Circle size indicates the relative sample size of each effect size. The fitted line is a simple least squares regression with the corresponding 95% confidence interval region in grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the publication year axis with minor random noise to reduce overlapping.

Publication year > 2015

Fitting a regression line over studies from 2015 to 2019, the decreasing tendency persists but it is less pronounced.

df_tidy_after2015 <- df_tidy[df_tidy$year >= 2015, ]

ggplot(df_tidy_after2015, aes(x = year, y = z2r(z))) + 
    geom_jitter(aes(size = n), shape = 21, 
                fill = alpha(plt_color, 0.5), 
                color = plt_color) + 
    geom_hline(yintercept=0, linetype = 2) +
    geom_smooth(method='lm', color = plt_color) +
    labs(y = "Effect size (r)", x = "Publication Year") +  
    scale_x_continuous(limits = c(2015, 2019),
                       breaks = seq(min(df_tidy_after2015$year), 
                                    max(df_tidy_after2015$year), 
                                    by = 1)) +
    theme_minimal() +
    theme(
        axis.line.y = element_line(color = "black"),
        axis.text = element_text(color = "black", size = 12),
        axis.title = element_text(color = "black", size = 14),
        legend.position = "none"
    )

Figure S13 - Relation between effect size mean values (circles) and publication year after 2015 (inclusive) where there is a sudden and prominent rise of publications. Circle size indicates the relative sample size of each effect size. The fitted line is a simple least squares regression with the corresponding 95% confidence interval region in grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the publication year axis with minor random noise to reduce overlapping.

Impact factor

Effect size values do not appear to exhibit a tendency when it comes to journal impact factor.

plt_color <- "deeppink4"
ggplot(df_tidy, aes(x = impact_factor, y = z2r(z))) + 
  geom_jitter(aes(size = n), shape = 21, 
              fill = alpha(plt_color, 0.5), 
              color = plt_color,
              width = 0.2) + 
    geom_hline(yintercept=0, linetype = 2) +
    geom_smooth(method='lm', color = plt_color) +
    labs(y = "Effect size (r)", x = "Impact Factor") +  
    theme_minimal() +
    theme(
        axis.line.y = element_line(color = "black"),
        axis.text = element_text(color = "black", size = 12),
        axis.title = element_text(color = "black", size = 14),
        legend.position = "none"
    )

Figure S14 - Relation between effect size mean values (circles) and journal impact factor. Circle size indicates the relative sample size of each effect size. The fitted line is a simple least squares regression with the corresponding 95% confidence interval region in grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the impact factor axis with minor random noise to reduce overlapping.

Supplementary data

All code files and supplementary data used in the study can be found in (ADD REPO LINK HERE)

Session info

sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Arch Linux
## 
## Matrix products: default
## BLAS:   /usr/lib/libopenblasp-r0.3.10.so
## LAPACK: /usr/lib/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] RColorBrewer_1.1-2 ggplot2_3.3.1      pander_0.6.3       kableExtra_1.1.0  
##  [5] MuMIn_1.43.17      compute.es_0.2-5   metafor_2.4-0      Matrix_1.2-18     
##  [9] stringr_1.4.0      dplyr_1.0.0        png_0.1-7          knitr_1.28        
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.1.0  xfun_0.14         purrr_0.3.4       splines_4.0.3    
##  [5] lattice_0.20-41   colorspace_1.4-1  vctrs_0.3.0       generics_0.0.2   
##  [9] htmltools_0.4.0   stats4_4.0.3      viridisLite_0.3.0 yaml_2.2.1       
## [13] mgcv_1.8-33       rlang_0.4.6       pillar_1.4.4      glue_1.4.1       
## [17] withr_2.2.0       jpeg_0.1-8.1      lifecycle_0.2.0   munsell_0.5.0    
## [21] gtable_0.3.0      rvest_0.3.5       codetools_0.2-16  evaluate_0.14    
## [25] labeling_0.3      highr_0.8         Rcpp_1.0.4.6      readr_1.3.1      
## [29] scales_1.1.1      webshot_0.5.2     farver_2.0.3      hms_0.5.3        
## [33] digest_0.6.25     stringi_1.4.6     grid_4.0.3        tools_4.0.3      
## [37] magrittr_1.5      tibble_3.0.1      crayon_1.3.4      pkgconfig_2.0.3  
## [41] ellipsis_0.3.1    xml2_1.3.2        rmarkdown_2.1     httr_1.4.1       
## [45] rstudioapi_0.11   R6_2.4.1          nlme_3.1-149      compiler_4.0.3

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